{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cefe0e7-7f83-4bfb-8949-81e3f85f21b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5041cd9a-0978-480f-a3b3-a6e5b1c7bdb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4188f9af-4459-466d-9f2e-2c77db3d4e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'datasets/bitly_usagov/example.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be40e7e8-310e-4d7d-9b8d-9ac5edfaf996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.78 Safari\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/www.facebook.com\\/l\\/7AQEFzjSi\\/1.usa.gov\\/wfLQtf\", \"u\": \"http:\\/\\/www.ncbi.nlm.nih.gov\\/pubmed\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(path) as f:\n",
    "    print(f.readline())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf4c376f-60e1-45ae-b19c-e390d834eee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5acb23ac-1f91-463e-b8b2-979a100398b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path,encoding='utf-8') as f:\n",
    "    records = [json.loads(line) for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31e95929-e978-4f3a-bc2a-dd24c2f64176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " 'c': 'US',\n",
       " 'nk': 1,\n",
       " 'tz': 'America/New_York',\n",
       " 'gr': 'MA',\n",
       " 'g': 'A6qOVH',\n",
       " 'h': 'wfLQtf',\n",
       " 'l': 'orofrog',\n",
       " 'al': 'en-US,en;q=0.8',\n",
       " 'hh': '1.usa.gov',\n",
       " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991',\n",
       " 't': 1331923247,\n",
       " 'hc': 1331822918,\n",
       " 'cy': 'Danvers',\n",
       " 'll': [42.576698, -70.954903]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c36db58-4501-4b3e-b915-a75cccd15b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_zones = [rec[\"tz\"] for rec in records if 'tz' in rec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99b6e4ae-c254-478c-9beb-4d0a6265f58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "337c0907-6918-4edd-8c2f-291d2f8cc695",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_counts(sequence):\n",
    "    counts={}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x]+=1\n",
    "        else:\n",
    "            counts[x] = 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fc5e1c0-e41d-411a-8fc3-a82ab4211895",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24554785-f3e4-40e6-acdd-6539f53d807a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_counts2(sequence):\n",
    "    counts = defaultdict(int)\n",
    "    for x in sequence:\n",
    "        counts[x] +=1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d0adfea4-2f77-4b58-8925-a9efc595dcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = get_counts(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15517cf7-003c-48ab-995f-4fd004de8cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts[\"America/New_York\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "890d7371-a465-4c9a-8d7c-3e6e85c551d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e27f6872-af0d-477a-8ee0-cd6dccefe791",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_counts(count_dict,n=10):\n",
    "    value_key_pairs = [(count,tz) for tz,count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0dd4a19-5b5b-422c-9c18-f5a67130d49d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33, 'America/Sao_Paulo'),\n",
       " (35, 'Europe/Madrid'),\n",
       " (36, 'Pacific/Honolulu'),\n",
       " (37, 'Asia/Tokyo'),\n",
       " (74, 'Europe/London'),\n",
       " (191, 'America/Denver'),\n",
       " (382, 'America/Los_Angeles'),\n",
       " (400, 'America/Chicago'),\n",
       " (521, ''),\n",
       " (1251, 'America/New_York')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "645f72b5-0bb4-4017-a835-aba77607c556",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "005e4425-91cf-41f7-a11a-7ebafb676fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = Counter(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2002afd2-cb69-4795-a2cf-8c99235129c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('America/New_York', 1251),\n",
       " ('', 521),\n",
       " ('America/Chicago', 400),\n",
       " ('America/Los_Angeles', 382),\n",
       " ('America/Denver', 191),\n",
       " ('Europe/London', 74),\n",
       " ('Asia/Tokyo', 37),\n",
       " ('Pacific/Honolulu', 36),\n",
       " ('Europe/Madrid', 35),\n",
       " ('America/Sao_Paulo', 33)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e5fd08b5-85e0-4eb7-bcf4-5cee972b58da",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b780b7f-25ad-4b8e-937b-e200daf5db91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3560 entries, 0 to 3559\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   a            3440 non-null   object \n",
      " 1   c            2919 non-null   object \n",
      " 2   nk           3440 non-null   float64\n",
      " 3   tz           3440 non-null   object \n",
      " 4   gr           2919 non-null   object \n",
      " 5   g            3440 non-null   object \n",
      " 6   h            3440 non-null   object \n",
      " 7   l            3440 non-null   object \n",
      " 8   al           3094 non-null   object \n",
      " 9   hh           3440 non-null   object \n",
      " 10  r            3440 non-null   object \n",
      " 11  u            3440 non-null   object \n",
      " 12  t            3440 non-null   float64\n",
      " 13  hc           3440 non-null   float64\n",
      " 14  cy           2919 non-null   object \n",
      " 15  ll           2919 non-null   object \n",
      " 16  _heartbeat_  120 non-null    float64\n",
      " 17  kw           93 non-null     object \n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 500.8+ KB\n"
     ]
    }
   ],
   "source": [
    "frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a3bc153b-379c-47a9-8a76-45ddd3394185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     America/New_York\n",
       "1       America/Denver\n",
       "2     America/New_York\n",
       "3    America/Sao_Paulo\n",
       "4     America/New_York\n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "948c6785-7e97-448c-acbd-b70c039474ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "tz_counts = frame['tz'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "494f434e-ecc5-4360-b969-dadbd4a93647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa579a34-1301-4150-a319-34a88ab7b624",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "14a1a72d-2e0c-4bd5-b64b-7e5d00076056",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_tz[clean_tz==''] = 'Unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db6b63d5-e3c5-4e05-969c-eee54aea3419",
   "metadata": {},
   "outputs": [],
   "source": [
    "tz_counts = clean_tz.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "879a690a-1096-4454-9d75-f52e1820bd97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251\n",
       "Unknown                 521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c37009d-6705-48ec-a02a-c94e9172ac3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b43d242e-99fd-43f6-8364-e9111b08c14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = tz_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3c5e951c-0fb5-4070-8f55-25dbe0a3c881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='tz'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=subset.index,x=subset.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4ddc7d23-4267-4cc1-b79f-176539757c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1505b8de-511d-4a61-9604-d146e3cb5591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[\"a\"][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8d28da67-359a-45fb-b570-d5e508042b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P9'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][51][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "656772b7-9a26-4bf1-89fc-b805e7b413dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.Series([x.split()[0] for x in frame['a'].dropna()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "68b5e945-7006-48e6-9182-f2cc70cca4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Mozilla/5.0\n",
       "1    GoogleMaps/RochesterNY\n",
       "2               Mozilla/4.0\n",
       "3               Mozilla/5.0\n",
       "4               Mozilla/5.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b5ecebcb-3c50-4d7b-bd9c-b5f03ab18555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0                 2594\n",
       "Mozilla/4.0                  601\n",
       "GoogleMaps/RochesterNY       121\n",
       "Opera/9.80                    34\n",
       "TEST_INTERNET_AGENT           24\n",
       "GoogleProducer                21\n",
       "Mozilla/6.0                    5\n",
       "BlackBerry8520/5.0.0.681       4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts().head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "14678a00-112a-4435-a9f1-ce9071193903",
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe = frame[frame['a'].notna()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f34b1beb-2dd7-4d3b-86e2-3b34bcb616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe['os'] = np.where(cframe['a'].str.contains('Windows'),'Windows','Not Windows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "15b1c3fe-a3cc-426e-84c4-a286656411db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Windows\n",
       "1    Not Windows\n",
       "2        Windows\n",
       "3    Not Windows\n",
       "4        Windows\n",
       "Name: os, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe['os'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a52d33e6-9832-456e-999b-52e12bf0000b",
   "metadata": {},
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz','os'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "426eb68e-0fae-4a47-8729-0ca049d18bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a9ee44b4-791e-460b-be67-1fcff2c26fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "                           245.0    276.0\n",
       "Africa/Cairo                 0.0      3.0\n",
       "Africa/Casablanca            0.0      1.0\n",
       "Africa/Ceuta                 0.0      2.0\n",
       "Africa/Johannesburg          0.0      1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "33a20764-85e5-4093-a3f4-9735daff730d",
   "metadata": {},
   "outputs": [],
   "source": [
    "indexer = agg_counts.sum('columns').argsort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1d738e13-13cf-40d1-b721-e80290c4801d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d71acfa4-e847-4bcd-b92a-c06017681dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_subset = agg_counts.take(indexer[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e60d7756-8bb8-4d91-a0d4-c282f12b79d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fec9a0f0-f3f1-4c6b-9893-8df2c6b14e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251.0\n",
       "                        521.0\n",
       "America/Chicago         400.0\n",
       "America/Los_Angeles     382.0\n",
       "America/Denver          191.0\n",
       "Europe/London            74.0\n",
       "Asia/Tokyo               37.0\n",
       "Pacific/Honolulu         36.0\n",
       "Europe/Madrid            35.0\n",
       "America/Sao_Paulo        33.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.sum(axis='columns').nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4310b6e1-0cc1-4d68-a6fb-94a810f001a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_subset = count_subset.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "044bb31f-773a-438a-9bcf-4ae7e21e0784",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_subset.name = 'total'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "721a7836-9c5c-4269-83d4-85c52689fb63",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_subset = count_subset.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "29b80e0f-5679-479b-963d-d31ef3619134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tz</th>\n",
       "      <th>os</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Windows</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Windows</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Windows</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tz           os  total\n",
       "0  America/Sao_Paulo  Not Windows   13.0\n",
       "1  America/Sao_Paulo      Windows   20.0\n",
       "2      Europe/Madrid  Not Windows   16.0\n",
       "3      Europe/Madrid      Windows   19.0\n",
       "4   Pacific/Honolulu  Not Windows    0.0\n",
       "5   Pacific/Honolulu      Windows   36.0\n",
       "6         Asia/Tokyo  Not Windows    2.0\n",
       "7         Asia/Tokyo      Windows   35.0\n",
       "8      Europe/London  Not Windows   43.0\n",
       "9      Europe/London      Windows   31.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9df024c2-4f4b-4c03-9545-e77bc2970d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='total', ylabel='tz'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='total',y='tz',hue='os',data=count_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fd3e211c-ec9f-4767-9eff-6645634e60a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_total(group):\n",
    "    group['normed_total'] = group['total']/group['total'].sum()\n",
    "    return group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cb8cc2e1-fa84-4ffa-b5fd-58b2f4ee04a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = count_subset.groupby('tz').apply(norm_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3089282e-6c22-4ad1-9854-2e3df16dd011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='normed_total', ylabel='tz'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YPn06RowYgbi4OJlx582bh6CgIKSkpMDe3r5UbVu3bsXEiRMxduxY3Lx5E1FRUbCwsBCPKykpYePGjbh16xbCwsJw5swZzJkzR2aMW7duITMzE927d8fjx49x/PhxTJw4EVpaWqWu9/rvVV6yZAl8fHxw48YN9O7dG8OHD8fjx4/LvI9JSUno0aMHbG1tkZiYiPj4ePTt2xcvXrwAAOTl5WHGjBm4cuUKYmJioKSkhIEDB4q/RjAnJwd9+/ZF69at8fPPP2PZsmWYO3euzDX+/PNP9O7dG+3atcP169exdetWhISEYPny5WXWVKKgoAA5OTkyLyIioppUU8+W+Pr6YsCAATVSU11VJ5cchISEYMSIEQAALy8vZGdnIy4uTub3EC9fvhwuLi4AAD8/P/j7+yM1NRXm5uYAgA8//BBnz57F3LlzUVBQgJUrV+L06dPo1KkTAMDc3Bzx8fHYvn07XF1dxXGXLl2Knj17llvb8uXLMXPmTEydOlVsa9eunfj1tGnTxK9NTU2xfPlyjBs3Dlu2bBHbIyMj4enpCTU1Ndy7dw+CIMDa2rpS98bX1xdDhw4FAKxcuRIbN27EpUuX4OXlVapvye9pfvXadnZ24teDBw+W6b9jxw40aNAAycnJaNWqFXbv3g2JRIJvvvkG6urqsLW1xZ9//okxY8aI52zZsgUmJibYvHkzJBIJrK2t8fDhQ8ydOxeLFi0q9/dCBwYGYsmSJaXaw7U3QEdduVL3guqOB0vXyLsEovdas0U35V3CW7dt2zbMnj0bWVlZUFF5GY9yc3NhYGAAFxcXxMbGin1jY2Ph7u6O27dv89mS90SdC7R37tzBpUuX8OOPPwIAVFRUMGTIEISEhMgE2ldnTxs1agRNTU0xzJa0Xbp0CQBw79495Ofnlwqqz58/h6Ojo0ybs7NzubVlZmbi4cOH6NGjR7l9Tp8+jcDAQNy+fRs5OTkoKirCs2fPkJ+fD01NTQAvA23JMoXXZ57f5NXPraWlBV1dXWRmZpbZNykpCR999FG5Y929exeLFi3CxYsX8c8//4gzsw8ePECrVq1w584d2Nvby/ze5vbt28uMkZKSgk6dOkEikYhtLi4uyM3NxR9//IFmzZqVeW1/f3/MmDFDfJ+TkyPzkBwREb1bD5a2fqfXq2oId3d3R25uLq5cuYKOHTsCAM6fP4/GjRvj4sWLePbsmfj96uzZs2jWrBmsrKxqvG6qnjq35CAkJARFRUUwNjaGiooKVFRUsHXrVhw8eBDZ2dliP1VVVfFriUQi876krSSglaxhjY6ORlJSkvhKTk6WWUcLoMwf+5fQ0NCosPa0tDR4e3vD3t4eBw8exNWrV/HVV18BeBmeASAjIwPXrl0Td29o2bIlJBJJpR/8quhzVrXevn374vHjx/jmm29w8eJFXLx4UabWt0kqlUJXV1fmRUREVB4rKysYGRmVmont378/zMzMZJ45KZmhfX3JQWhoKPT19XHixAnY2NhAW1sbXl5eyMjIEM998eIFZsyYIT5fMmfOnFKTTwUFBZgyZQoaNmwIdXV1dOnSBZcvXxaPOzs7Y+3ateL7AQMGQFVVVcwjf/zxByQSCe7duwfg5U87W7ZsCXV1dTRq1Agffvhhjd2390WdCrRFRUXYtWsXgoODZYLn9evXYWxsjD179lRrXFtbW0ilUjx48AAWFhYyr6rMCuro6MDU1LTcrbSuXr2K4uJiBAcHo2PHjrC0tMTDhw9l+hw+fBidO3eGoaEhAMDQ0BCenp746quvkJeXV2rM/7Lux97evtxaHz16hDt37mDBggXo0aMHbGxsxIfFSlhZWeHmzZsoKCgQ2179DxYAbGxskJiYKPMfe0JCAnR0dNC0adNq105ERPQ6d3d3nD17Vnx/9uxZuLm5wdXVVWz/999/cfHiRbi7u5c5Rn5+PtauXYvvvvsO586dw4MHDzBr1izxeHBwMEJDQ7Fjxw7Ex8fj8ePH4k+NS8yZMwcHDx5EWFgYfv75Z1hYWMDT01N8psXV1VUM3oIg4Pz589DX10d8fDyAlw9TN2nSBBYWFrhy5QqmTJmCpUuX4s6dOzh+/Di6detWY/fsfVGnAu2RI0eQlZUFPz8/tGrVSuY1ePBghISEVGtcHR0dzJo1C9OnT0dYWBhSU1Px888/Y9OmTQgLC6vSWAEBAQgODsbGjRtx9+5dcRwAsLCwQGFhITZt2oTffvsN3333HbZt2yZz/qu7G5T46quv8OLFC7Rv3x4HDx7E3bt3kZKSgo0bN4prfqvD398fly9fxoQJE3Djxg3cvn0bW7duxT///AMDAwPUq1cPX3/9Ne7du4czZ87ILAEAgGHDhqG4uBhjx45FSkoKTpw4If6Ls2SJwYQJE5Ceno7Jkyfj9u3biIyMxOLFizFjxoxy188SERFVh7u7OxISElBUVISnT5/i2rVrcHV1Rbdu3cQAmZiYiIKCgnIDbWFhIbZt2wZnZ2c4OTlh0qRJMpM/69evh7+/PwYNGgQbGxts27ZNZg1uXl4etm7dijVr1qBXr16wtbXFN998Aw0NDTGnuLm5IT4+Hi9evMCNGzegpqaG4cOHizXGxsaKz+88ePAAWlpa8Pb2RvPmzeHo6IgpU6a8hbsnX3UqEYSEhMDDw6PMxduDBw/GlStXcOPGjWqNvWzZMixcuBCBgYGwsbGBl5cXoqOjYWZmVqVxRo0ahfXr12PLli2ws7ODt7c37t69CwBo06YN1q1bh1WrVqFVq1YIDw9HYGCgeG5eXh5iYmJKBVpzc3P8/PPPcHd3x8yZM9GqVSv07NkTMTEx2Lp1a7U+LwBYWlri5MmTuH79Otq3b49OnTohMjISKioqUFJSwt69e3H16lW0atUK06dPx5o1sg/e6Orq4vDhw0hKSoKDgwPmz5+PRYsWAYC4TqlJkyY4evQoLl26hDZt2mDcuHHw8/PDggULql03ERFRWdzc3JCXl4fLly/j/PnzsLS0RIMGDeDq6iquo42NjYW5uXm5z3BoamqiRYsW4nsjIyPxWZTs7GxkZGSgQ4cO4nEVFRWZ52tSU1NRWFgoPpgOvFwO2L59e6SkpAAAunbtKgbuuLg4uLq6ws3NTQy0rz7o3rNnTzRv3hzm5uYYOXIkwsPDZbYdrS0kQlWfGqL31qFDh7BgwQIkJyfLu5RqCw8Px6effors7Ow3rtGtipycHOjp6eEXfxvuckBEVEVVecDq2bNnuH//PszMzGQe+n3fHworYWJigvHjxyMrKwt5eXniTj4tW7bE9u3bsXjxYlhbW+Obb74R19JmZWVBX18foaGhmDZtmsxyvoiICAwcOBCCICA7Oxv6+vqIi4uT+bF/yfGIiAjcuHEDbdq0QVpaGpo3by7Tx8DAADt27AAAODk5YdiwYUhMTETPnj3h4+MDIyMj3Lx5E1ZWVrhz5w4sLS0BvFxyGRsbi5MnT+LgwYNQUlLC5cuXS23dKS/l/Z0B/v/37+zs7Aqfh6lTM7S1nba2NlatWiXvMqpk165diI+Px/379xEREYG5c+fCx8enRsMsERFRZZU87BUbGyuz+1G3bt1w7NgxXLp0qdzlBm+ip6cHIyMj8SFp4GXYvHr1qvi+RYsWUFNTQ0JCgthWWFiIy5cvw9bWVmwrWdd77tw5uLm5wdDQEDY2NlixYgWMjIzEMAu8nAX28PDA6tWrcePGDaSlpeHMmTPV+gzvqzq3bVdt9sEHH8i7hCr766+/sGjRIvz1118wMjLCRx99hBUrVsi7LCIiqqPc3d0xceJEFBYWyuwj7+rqikmTJuH58+fVDrQAMHXqVAQFBaFly5awtrbGunXrZGZ0tbS0MH78eMyePRuGhoZo1qwZVq9ejfz8fPj5+Yn93NzcsGnTJjRo0EDca97NzQ2bN2+W2VLzyJEj+O2339CtWzcYGBjg6NGjKC4urnVbjjHQklzNmTOn1G86IyIikhd3d3f8+++/sLa2RqNGjcR2V1dXPH36VNzeq7pmzpyJjIwMjBo1CkpKSvjss88wcOBAma1Dg4KCUFxcjJEjR+Lp06dwdnbGiRMnYGBgIPbp2rUriouLZUK3m5sbNmzYIDOzrK+vj0OHDiEgIADPnj1Dy5YtsWfPHplfhFQbcA0t1QlcQ0tEVH01sYaWqDxcQ0tEREREdR4DLREREREpNAZaIiIiIlJoDLREREREpNC4ywHVKSbzfqpwUTkREREpHs7QEhERUY3jJkpUWTXxd4WBloiIiGqMqqoqACA/P1/OlZCiKPm7UvJ3pzq45ICIiIhqjLKyMvT19ZGZmQkA0NTUhEQikXNV9D4SBAH5+fnIzMyEvr4+lJWrv088Ay0RERHVqMaNGwOAGGqJKqKvry/+nakuBloiIiKqURKJBEZGRmjYsCEKCwvlXQ69x1RVVf/TzGwJBloiIiJ6K5SVlWskrBC9CR8KIyIiIiKFxhlaqlO6LdgDZamGvMsgInovXF3zibxLIKoRnKElIiIiIoXGQEtERERECo2BloiIiIgUGgMtERERESk0BloiIiIiUmgMtERERESk0BhoiYiIiEihMdASERERkUJjoH0PBQQEwMHBQd5lEBERESkEBtpXJCYmQllZGX369JFrHbNmzUJMTEyNjxsXFwcTExMAgK+vLyQSCSQSCVRVVdGoUSP07NkTO3bsQHFxcY1fm4iIiOhtYaB9RUhICCZPnoxz587h4cOH7/z6giCgqKgI2traqFevXo2PHxkZib59+4rvvby8kJGRgbS0NBw7dgzu7u6YOnUqvL29UVRUVOPXr2mFhYXyLoGIiIjeAwy0/yc3Nxf79u3D+PHj0adPH4SGhorHYmNjIZFIcOLECTg6OkJDQwPdu3dHZmYmjh07BhsbG+jq6mLYsGHIz88XzysuLkZgYCDMzMygoaGBNm3a4MCBA6XGPXbsGNq2bQupVIr4+Pgylxzs2LEDdnZ2kEqlMDIywqRJk8Rj69atQ+vWraGlpQUTExNMmDABubm5pT5jVFQU+vXrJ76XSqVo3LgxmjRpAicnJ3zxxReIjIzEsWPHZD7/kydPMHr0aDRo0AC6urro3r07rl+/Lh4vqfe7776Dqakp9PT08PHHH+Pp06cAgK+//hrGxsalZn779++Pzz77THwfGRkJJycnqKurw9zcHEuWLJEJ1hKJBFu3bkW/fv2gpaWFFStWlPfHSURERHUIA+3/2b9/P6ytrWFlZYURI0Zgx44dEARBpk9AQAA2b96MCxcuID09HT4+Pli/fj12796N6OhonDx5Eps2bRL7BwYGYteuXdi2bRtu3bqF6dOnY8SIEYiLi5MZd968eQgKCkJKSgrs7e1L1bZ161ZMnDgRY8eOxc2bNxEVFQULCwvxuJKSEjZu3Ihbt24hLCwMZ86cwZw5c2TGuHXrFjIzM9G9e/cK70P37t3Rpk0bHDp0SGz76KOPxPB+9epVODk5oUePHnj8+LHYJzU1FREREThy5AiOHDmCuLg4BAUFiec/evQIZ8+eFfs/fvwYx48fx/DhwwEA58+fxyeffIKpU6ciOTkZ27dvR2hoaKnQGhAQgIEDB+LmzZsyYfh1BQUFyMnJkXkRERFR7aQi7wLeFyEhIRgxYgSAlz+Kz87ORlxcHNzc3MQ+y5cvh4uLCwDAz88P/v7+SE1Nhbm5OQDgww8/xNmzZzF37lwUFBRg5cqVOH36NDp16gQAMDc3R3x8PLZv3w5XV1dx3KVLl6Jnz57l1rZ8+XLMnDkTU6dOFdvatWsnfj1t2jTxa1NTUyxfvhzjxo3Dli1bxPbIyEh4enpCTU3tjffC2toaN27cAADEx8fj0qVLyMzMhFQqBQCsXbsWEREROHDgAMaOHQvg5Wx0aGgodHR0AAAjR45ETEwMVqxYAQMDA/Tq1Qu7d+9Gjx49AAAHDhxA/fr14e7uDgBYsmQJ5s2bh1GjRon3atmyZZgzZw4WL14s1jZs2DB8+umnb/wMgYGBWLJkSan2cO0N0FFXfuP5RGVptuimvEsgIqIycIYWwJ07d3Dp0iUMHToUAKCiooIhQ4YgJCREpt+rs6eNGjWCpqamGGZL2jIzMwEA9+7dQ35+Pnr27AltbW3xtWvXLqSmpsqM6+zsXG5tmZmZePjwoRgEy3L69Gn06NEDTZo0gY6ODkaOHIlHjx7JLH+IjIyUWW5QEUEQIJFIAADXr19Hbm4u6tWrJ/M57t+/L/M5TE1NxTALAEZGRuK9AIDhw4fj4MGDKCgoAACEh4fj448/hpKSknidpUuXylxjzJgxyMjIkPkcFd2rV/n7+yM7O1t8paenV+o8IiIiUjycocXL2dmioiIYGxuLbYIgQCqVYvPmzWKbqqqq+HXJ7gCvkkgk4jrRkjWs0dHRaNKkiUy/kpnOElpaWuXWpqGhUWHtaWlp8Pb2xvjx47FixQoYGhoiPj4efn5+eP78OTQ1NZGRkYFr165VeveGlJQUmJmZiZ/DyMgIsbGxpfrp6+uLX1d0LwCgb9++EAQB0dHRaNeuHc6fP48vv/xSPJ6bm4slS5Zg0KBBpa6jrq4ufl3RvXqVVCotdZ+JiIiodqrzgbaoqAi7du1CcHAwPvjgA5ljAwYMwJ49e2BtbV3lcW1tbSGVSvHgwQOZ5QVVpaOjA1NTU8TExIg/nn/V1atXUVxcjODgYHG2c//+/TJ9Dh8+jM6dO8PQ0PCN1ztz5gxu3ryJ6dOnAwCcnJzw119/QUVFBaamptX+HOrq6hg0aBDCw8Nx7949WFlZwcnJSTzu5OSEO3fuyKwNJiIiIqqMOh9ojxw5gqysLPj5+UFPT0/m2ODBgxESEoI1a9ZUeVwdHR3MmjUL06dPR3FxMbp06YLs7GwkJCRAV1dXXCtaGQEBARg3bhwaNmyIXr164enTp0hISMDkyZNhYWGBwsJCbNq0CX379kVCQgK2bdsmc/7ruxuUKCgowF9//YUXL17g77//xvHjxxEYGAhvb2988sknAAAPDw906tQJAwYMwOrVq2FpaYmHDx8iOjoaAwcOrPQSAODlsgNvb2/cunVLXK9cYtGiRfD29kazZs3w4YcfQklJCdevX8cvv/yC5cuXV/oaREREVPfU+TW0ISEh8PDwKBVmgZeB9sqVK+IDUlW1bNkyLFy4EIGBgbCxsYGXlxeio6PFH+dX1qhRo7B+/Xps2bIFdnZ28Pb2xt27dwEAbdq0wbp167Bq1Sq0atUK4eHhCAwMFM/Ny8tDTExMmYH2+PHjMDIygqmpKby8vHD27Fls3LgRkZGRUFZ++eCURCLB0aNH0a1bN3z66aewtLTExx9/jN9//x2NGjWq0ufo3r07DA0NcefOHQwbNkzmmKenJ44cOYKTJ0+iXbt26NixI7788ks0b968StcgIiKiukcivL43FdUqhw4dwoIFC5CcnCzvUuQqJycHenp6+MXfhrscULVxlwMioner5Pt3dnY2dHV1y+1X52doazttbW2sWrVK3mUQERERvTV1fg1tbff6g25EREREtQ1naImIiIhIoTHQEhEREZFCY6AlIiIiIoXGQEtERERECo2BloiIiIgUGgMtERERESk0bttFdYrJvJ8q3JiZiIiIFA9naImIiIhIoTHQEhEREZFCY6AlIiIiIoXGQEtERERECo2BloiIiIgUGgMtERERESk0BloiIiIiUmjch5bqlG4L9kBZqiHvMqgOurrmE3mXQERUa3GGloiIiIgUGgMtERERESk0BloiIiIiUmgMtERERESk0BhoiYiIiEihMdASERERkUJjoCUiIiIihcZAS0REREQKjYH2FQEBAXBwcJB3GXWCRCJBRESEvMsgIiKiWuC9CLSJiYlQVlZGnz595FrHrFmzEBMTU+PjxsXFwcTEBADg6+uLAQMG1Pg1KuuPP/6AmpoaWrVqJbcaiIiIiGrSexFoQ0JCMHnyZJw7dw4PHz5859cXBAFFRUXQ1tZGvXr1anz8yMhI9O3bt8bHrY7Q0FD4+PggJycHFy9elHc5RERERP+Z3ANtbm4u9u3bh/Hjx6NPnz4IDQ0Vj8XGxkIikeDEiRNwdHSEhoYGunfvjszMTBw7dgw2NjbQ1dXFsGHDkJ+fL55XXFyMwMBAmJmZQUNDA23atMGBAwdKjXvs2DG0bdsWUqkU8fHxZS452LFjB+zs7CCVSmFkZIRJkyaJx9atW4fWrVtDS0sLJiYmmDBhAnJzc0t9xqioKPTr169S9yMuLg7t27cXrzdv3jwUFRWJxw8cOIDWrVtDQ0MD9erVg4eHB/Ly8io1tiAI2LlzJ0aOHIlhw4YhJCRE5nhaWhokEgkOHToEd3d3aGpqok2bNkhMTJTp980338DExASampoYOHAg1q1bB319fZk+kZGRcHJygrq6OszNzbFkyRKZz/G69PR0+Pj4QF9fH4aGhujfvz/S0tLE47GxsWjfvj20tLSgr68PFxcX/P7775X63ERERFS7yT3Q7t+/H9bW1rCyssKIESOwY8cOCIIg0ycgIACbN2/GhQsXxOCzfv167N69G9HR0Th58iQ2bdok9g8MDMSuXbuwbds23Lp1C9OnT8eIESMQFxcnM+68efMQFBSElJQU2Nvbl6pt69atmDhxIsaOHYubN28iKioKFhYW4nElJSVs3LgRt27dQlhYGM6cOYM5c+bIjHHr1i1kZmaie/fub7wXf/75J3r37o127drh+vXr2Lp1K0JCQrB8+XIAQEZGBoYOHYrPPvsMKSkpiI2NxaBBg0rdr/KcPXsW+fn58PDwwIgRI7B3794yw/D8+fMxa9YsJCUlwdLSEkOHDhXDaEJCAsaNG4epU6ciKSkJPXv2xIoVK2TOP3/+PD755BNMnToVycnJ2L59O0JDQ0v1K1FYWAhPT0/o6Ojg/PnzSEhIgLa2Nry8vPD8+XMUFRVhwIABcHV1xY0bN5CYmIixY8dCIpGU+1kLCgqQk5Mj8yIiIqLaSSJUNg29JS4uLvDx8cHUqVNRVFQEIyMj/PDDD3Bzc0NsbCzc3d1x+vRp9OjRAwAQFBQEf39/pKamwtzcHAAwbtw4pKWl4fjx4ygoKIChoSFOnz6NTp06idcZPXo08vPzsXv3bnHciIgI9O/fX+wTEBCAiIgIJCUlAQCaNGmCTz/9VAyUb3LgwAGMGzcO//zzj9i2cuVKXLt2DT/88AOAl2tonzx5UuYDUfPnz8fBgweRkpIihrUtW7Zg7ty5yM7ORlJSEtq2bYu0tDQ0b9688jf5/wwfPhwNGzbEl19+CQBwcHDAtGnT4OvrC+DlDK2ZmRm+/fZb+Pn5AQCSk5NhZ2eHlJQUWFtb4+OPP0Zubi6OHDkijjtixAgcOXIET548AQB4eHigR48e8Pf3F/t8//33mDNnjrikRCKR4Mcff8SAAQPw/fffY/ny5TKf+/nz59DX10dERAScnZ1Rr149xMbGwtXVtVKfNSAgAEuWLCnV/ou/DXTUlat244iI3mPNFt2UdwlEb01OTg709PSQnZ0NXV3dcvvJdYb2zp07uHTpEoYOHQoAUFFRwZAhQ0r9KPzV2dNGjRpBU1NTDLMlbZmZmQCAe/fuIT8/Hz179oS2trb42rVrF1JTU2XGdXZ2Lre2zMxMPHz4UAzSZSkJ2k2aNIGOjg5GjhyJR48eySx/iIyMrPRyg5SUFHTq1Elm5tHFxQW5ubn4448/0KZNG/To0QOtW7fGRx99hG+++QZZWVmVGvvJkyc4dOgQRowYIbaNGDGi1L0GZO+3kZERAIj3986dO2jfvr1M/9ffX79+HUuXLpW5/2PGjEFGRobMvXm1/71796CjoyP2NzQ0xLNnz5CamgpDQ0P4+vrC09MTffv2xYYNG5CRkVHh5/X390d2drb4Sk9Pf8MdIiIiIkWlIs+Lh4SEoKioCMbGxmKbIAiQSqXYvHmz2Kaqqip+LZFIZN6XtBUXFwOAuIY1OjoaTZo0keknlUpl3mtpaZVbm4aGRoW1p6WlwdvbG+PHj8eKFStgaGiI+Ph4+Pn54fnz59DU1ERGRgauXbtWY7s3KCsr49SpU7hw4YK4zGL+/Pm4ePEizMzMKjx39+7dePbsGTp06CC2CYKA4uJi/Prrr7C0tBTbX7/fAMT7Wxm5ublYsmQJBg0aVOqYurp6mf3btm2L8PDwUscaNGgAANi5cyemTJmC48ePY9++fViwYAFOnTqFjh07llmDVCot9edNREREtZPcAm1RURF27dqF4OBgfPDBBzLHBgwYgD179sDa2rrK49ra2kIqleLBgweV/vF0WXR0dGBqaoqYmBi4u7uXOn716lUUFxcjODgYSkovJ7r3798v0+fw4cPo3LkzDA0NK3VNGxsbHDx4EIIgiEEyISEBOjo6aNq0KYCXAdPFxQUuLi5YtGgRmjdvjh9//BEzZsyocOyQkBDMnDlTXF5QYsKECdixYweCgoIqVaOVlRUuX74s0/b6eycnJ9y5c0dmvXFFnJycsG/fPjRs2LDCHyc4OjrC0dER/v7+6NSpE3bv3l1uoCUiIqK6Q26B9siRI8jKyoKfnx/09PRkjg0ePBghISFYs2ZNlcfV0dHBrFmzMH36dBQXF6NLly7Izs5GQkICdHV1MWrUqEqPFRAQgHHjxqFhw4bo1asXnj59ioSEBEyePBkWFhYoLCzEpk2b0LdvXyQkJGDbtm0y55e3u0HJethX1atXDxMmTMD69esxefJkTJo0CXfu3MHixYsxY8YMKCkp4eLFi4iJicEHH3yAhg0b4uLFi/jf//4HGxubCj9HUlISfv75Z4SHh5f6R8LQoUOxdOnSSq8Tnjx5Mrp164Z169ahb9++OHPmDI4dOyazTGLRokXw9vZGs2bN8OGHH0JJSQnXr1/HL7/8UuZ1hg8fjjVr1qB///5YunQpmjZtit9//x2HDh3CnDlzUFhYiK+//hr9+vWDsbEx7ty5g7t37+KTTz6pVM1ERERUu8ltDW1ISAg8PDxKhVngZaC9cuUKbty4Ua2xly1bhoULFyIwMBA2Njbw8vJCdHT0G38s/7pRo0Zh/fr12LJlC+zs7ODt7Y27d+8CANq0aYN169Zh1apVaNWqFcLDwxEYGCiem5eXh5iYmDIDbWxsrDjbWPJasmQJmjRpgqNHj+LSpUto06YNxo0bBz8/PyxYsAAAoKuri3PnzqF3796wtLTEggULEBwcjF69elX4OUJCQmBra1vmjPfAgQORmZmJo0ePVuqeuLi4YNu2bVi3bh3atGmD48ePY/r06TJLCTw9PXHkyBGcPHkS7dq1Q8eOHfHll1+W+yCbpqYmzp07h2bNmmHQoEGwsbGBn58fnj17Bl1dXWhqauL27dsYPHgwLC0tMXbsWEycOBGff/55pWomIiKi2k3uuxzUVocOHcKCBQuQnJws71LeujFjxuD27ds4f/68vEspV8lTktzlgIhqG+5yQLVZZXc5kOtDYbWZtrY2Vq1aJe8y3oq1a9eiZ8+e0NLSwrFjxxAWFoYtW7bIuywiIiKqoxho35LXH3R727S1tcs9duzYMXTt2rXGrnXp0iWsXr0aT58+hbm5OTZu3IjRo0fX2PhEREREVcFAW0u8/pDZq17fvuy/en03ByIiIiJ5YqCtJSq7RRYRERFRbSPX3xRGRERERPRfMdASERERkUJjoCUiIiIihcZAS0REREQKjQ+FUZ1iMu+nCjdmJiIiIsXDGVoiIiIiUmgMtERERESk0BhoiYiIiEihMdASERERkUJjoCUiIiIihcZAS0REREQKjYGWiIiIiBQa96GlOqXbgj1QlmrIuwySg6trPpF3CURE9JZwhpaIiIiIFBoDLREREREpNAZaIiIiIlJoDLREREREpNAYaImIiIhIoTHQEhEREZFCY6AlIiIiIoXGQEtERERECo2B9i0JCAiAg4ODvMuoFdzc3DBt2jR5l0FERETvqVoXaBMTE6GsrIw+ffrItY5Zs2YhJiamxseNi4uDiYkJAMDX1xcSiQRBQUEyfSIiIiCRSGr82m+ybNkyGBkZ4fHjxzLt169fh1QqxZEjR955TURERFT71bpAGxISgsmTJ+PcuXN4+PDhO7++IAgoKiqCtrY26tWrV+PjR0ZGom/fvuJ7dXV1rFq1CllZWTV+rary9/eHiYkJJk6cKLYVFhZi1KhRGDFiBLy9vas03vPnz2u6RCIiIqqFalWgzc3Nxb59+zB+/Hj06dMHoaGh4rHY2FhIJBKcOHECjo6O0NDQQPfu3ZGZmYljx47BxsYGurq6GDZsGPLz88XziouLERgYCDMzM2hoaKBNmzY4cOBAqXGPHTuGtm3bQiqVIj4+vswlBzt27ICdnR2kUimMjIwwadIk8di6devQunVraGlpwcTEBBMmTEBubm6pzxgVFYV+/fqJ7z08PNC4cWMEBgZWeG/i4+PRtWtXaGhowMTEBFOmTEFeXh4AYPPmzWjVqpXYt2SGd9u2bTLXWbBgQYXXUFFRwa5duxARESHeoxUrVuDJkyf48ssv8eDBA/Tv3x/a2trQ1dWFj48P/v77b/H8knv27bffwszMDOrq6mVeJzo6Gnp6eggPD6+wHiIiIqobalWg3b9/P6ytrWFlZYURI0Zgx44dEARBpk9AQAA2b96MCxcuID09HT4+Pli/fj12796N6OhonDx5Eps2bRL7BwYGYteuXdi2bRtu3bqF6dOnY8SIEYiLi5MZd968eQgKCkJKSgrs7e1L1bZ161ZMnDgRY8eOxc2bNxEVFQULCwvxuJKSEjZu3Ihbt24hLCwMZ86cwZw5c2TGuHXrFjIzM9G9e3exTVlZGStXrsSmTZvwxx9/lHlfUlNT4eXlhcGDB+PGjRvYt28f4uPjxUDt6uqK5ORk/O9//wPwcllD/fr1ERsbC+DlLGtiYiLc3Nze8CcAWFtbIzAwEOPHj8eJEycQGBiInTt3QltbG/3798fjx48RFxeHU6dO4bfffsOQIUNkzr937x4OHjyIQ4cOISkpqdT4u3fvxtChQxEeHo7hw4eXW0dBQQFycnJkXkRERFQ7SYTXE58Cc3FxgY+PD6ZOnYqioiIYGRnhhx9+gJubG2JjY+Hu7o7Tp0+jR48eAICgoCD4+/sjNTUV5ubmAIBx48YhLS0Nx48fR0FBAQwNDXH69Gl06tRJvM7o0aORn5+P3bt3i+NGRESgf//+Yp+AgABERESIoaxJkyb49NNPsXz58kp9lgMHDmDcuHH4559/xLaVK1fi2rVr+OGHHwC8XEP75MkTREREoFOnTrC1tUVISAgiIiIwcOBAMcyPHj0aysrK2L59uzhWfHw8XF1dkZeXB6lUigYNGmDbtm348MMP4ejoiCFDhmDDhg3IyMhAQkIC3N3d8eTJE2hqar6xdkEQ0L17d5w7dw6TJ0/G+vXrcerUKfTq1Qv3798X1wAnJyfDzs4Oly5dQrt27RAQEICVK1fizz//RIMGDcTx3Nzc4ODggJYtW2L+/PmIjIyEq6trhTUEBARgyZIlpdp/8beBjrryGz8DERFVXrNFN+VdAtVSOTk50NPTQ3Z2NnR1dcvtV2tmaO/cuYNLly5h6NChAF7++HvIkCEICQmR6ffq7GmjRo2gqakphtmStszMTAAvZwvz8/PRs2dPaGtri69du3YhNTVVZlxnZ+dya8vMzMTDhw/FIF2WkqDdpEkT6OjoYOTIkXj06JHM8ofIyEiZ5QavWrVqFcLCwpCSklLq2PXr1xEaGirzGTw9PVFcXIz79+9DIpGgW7duiI2NxZMnT5CcnIwJEyagoKAAt2/fRlxcHNq1a1epMAsAEokE8+fPR3FxsbhMISUlBSYmJmKYBQBbW1vo6+vL1Ny8eXOZMFviwIEDmD59Ok6dOvXGMAu8XM+bnZ0tvtLT0ytVOxERESkeFXkXUFNCQkJQVFQEY2NjsU0QBEilUmzevFlsU1VVFb+WSCQy70vaiouLAUBcwxodHY0mTZrI9JNKpTLvtbS0yq1NQ0OjwtrT0tLg7e2N8ePHY8WKFTA0NER8fDz8/Pzw/PlzaGpqIiMjA9euXSt394Zu3brB09MT/v7+8PX1lTmWm5uLzz//HFOmTCl1XrNmzQC8nAX9+uuvcf78eTg6OkJXV1cMuXFxcZUKka9SUVGR+d/KKu8+Ojo64ueff8aOHTvg7Oz8xl0cpFJpqT8jIiIiqp1qRaAtKirCrl27EBwcjA8++EDm2IABA7Bnzx5YW1tXeVxbW1tIpVI8ePCgyoHuVTo6OjA1NUVMTAzc3d1LHb969SqKi4sRHBwMJaWXk+b79++X6XP48GF07twZhoaG5V4nKCgIDg4OsLKykml3cnJCcnKyzJrd17m6umLatGniEg3gZcg9ffo0EhISMHPmzMp+3DLZ2NggPT0d6enpMksOnjx5Altb2zee36JFCwQHB8PNzQ3Kysoy/0ghIiKiuq1WBNojR44gKysLfn5+0NPTkzk2ePBghISEYM2aNVUeV0dHB7NmzcL06dNRXFyMLl26IDs7GwkJCdDV1cWoUaMqPVZAQADGjRuHhg0bolevXnj69CkSEhIwefJkWFhYoLCwEJs2bULfvn2RkJAgs8MAUHp3g7K0bt0aw4cPx8aNG2Xa586di44dO2LSpEkYPXo0tLS0kJycjFOnTonB0N7eHgYGBti9e7e4X6ybmxtmzZoFiUQCFxeXSn/Wsnh4eIj1rV+/HkVFRZgwYQJcXV0rXK7xKktLS5w9exZubm5QUVHB+vXr/1NNREREVDvUijW0ISEh8PDwKBVmgZeB9sqVK7hx40a1xl62bBkWLlyIwMBA2NjYwMvLC9HR0TAzM6vSOKNGjcL69euxZcsW2NnZwdvbG3fv3gUAtGnTBuvWrcOqVavQqlUrhIeHy2zDlZeXh5iYmDcGWgBYunSpuGSihL29PeLi4vDrr7+ia9eucHR0xKJFi2SWZ0gkEnTt2hUSiQRdunQRz9PV1YWzs3OFSyoqQyKRIDIyEgYGBujWrRs8PDxgbm6Offv2VWkcKysrnDlzBnv27PnPs8ZERERUO9SqXQ5qq0OHDmHBggVITk6WdykKq+QpSe5yQERU87jLAb0tdW6Xg9pMW1sbq1atkncZRERERO+lWrGGtrZ7/UE3ebKzs8Pvv/9e5rHt27dX+MsOiIiIiN4GBlqqkqNHj6KwsLDMY40aNXrH1RAREREx0FIVNW/eXN4lEBEREcngGloiIiIiUmgMtERERESk0BhoiYiIiEihVTnQnjt3DpmZmaXaCwsLce7cuRopioiIiIiosqr8ixWUlJTQqFEj/Pjjj+jYsaPY/vfff8PY2BgvXryo8SKJ/qvKbsxMRERE74+3+osVPv74Y/To0QOhoaEy7fylY0RERET0rlU50EokEvj7++O7777DpEmTMGPGDDHISiSSGi+QiIiIiKgiVQ60JeF10KBBOH/+PA4cOIBevXrhyZMnNV0bEREREdEb/addDhwdHXHp0iU8efIEPXr0qKmaiIiIiIgqrcqBdtSoUdDQ0BDfN27cGHFxcXB3d+dvkSIiIiKid67KuxwoKysjIyMDDRs2lGn/559/0KhRI+5yQO8l7nJARESkeCr7/VulqgMLglDmw195eXkyM7dE76NuC/ZAWcq/p1R7XV3zibxLICJ65yodaGfMmAHg5U4GCxcuhKampnjsxYsXuHjxItq0aVPzFRIRERERVaDSgfbatWsAXs7Q3rx5E2pqauIxNTU1tGnTBrNmzar5ComIiIiIKlDpQHv27FkAwKeffooNGzZwHSIRERERvReqvIZ2586db6MOIiIiIqJq+U/70BIRERERyRsDLREREREpNAZaIiIiIlJoDLREREREpNAYaImIiIhIoTHQ1pCAgAA4ODjIu4xawc3NDdOmTZN3GURERKQgFD7QJiYmQllZGX369JFrHbNmzUJMTEyNjxsXFwcTExMAwP/+9z+MHz8ezZo1g1QqRePGjeHp6YmEhIQav2553NzcIJFIIJFIoK6uDltbW2zZsuWdXZ+IiIjodQofaENCQjB58mScO3cODx8+fOfXFwQBRUVF0NbWRr169Wp8/MjISPTt2xcAMHjwYFy7dg1hYWH49ddfERUVBTc3Nzx69KjGr1uRMWPGICMjA8nJyfDx8cHEiROxZ8+ed1oDERERUQmFDrS5ubnYt28fxo8fjz59+iA0NFQ8FhsbC4lEghMnTsDR0REaGhro3r07MjMzcezYMdjY2EBXVxfDhg1Dfn6+eF5xcTECAwNhZmYGDQ0NtGnTBgcOHCg17rFjx9C2bVtIpVLEx8eXueRgx44dsLOzg1QqhZGRESZNmiQeW7duHVq3bg0tLS2YmJhgwoQJyM3NLfUZo6Ki0K9fPzx58gTnz5/HqlWr4O7ujubNm6N9+/bw9/dHv379qjTuwYMHxbpMTU0RHBxcpfuuqamJxo0bw9zcHAEBAWjZsiWioqIAAHPnzoWlpSU0NTVhbm6OhQsXorCwUDzX19cXAwYMkBlv2rRpcHNzK/d6WVlZ+OSTT2BgYABNTU306tULd+/erVLNREREVHspdKDdv38/rK2tYWVlhREjRmDHjh0QBEGmT0BAADZv3owLFy4gPT0dPj4+WL9+PXbv3o3o6GicPHkSmzZtEvsHBgZi165d2LZtG27duoXp06djxIgRiIuLkxl33rx5CAoKQkpKCuzt7UvVtnXrVkycOBFjx47FzZs3ERUVBQsLC/G4kpISNm7ciFu3biEsLAxnzpzBnDlzZMa4desWMjMz0b17d2hra0NbWxsREREoKCgo9568adyrV6/Cx8cHH3/8MW7evImAgAAsXLhQ5h8DVaWhoYHnz58DAHR0dBAaGork5GRs2LAB33zzDb788stqjw28DMFXrlxBVFQUEhMTIQgCevfuLROUX1dQUICcnByZFxEREdVOVf7Vt++TkJAQjBgxAgDg5eWF7OxsxMXFycz2LV++HC4uLgAAPz8/+Pv7IzU1Febm5gCADz/8EGfPnsXcuXNRUFCAlStX4vTp0+jUqRMAwNzcHPHx8di+fTtcXV3FcZcuXYqePXuWW9vy5csxc+ZMTJ06VWxr166d+PWrDz2Zmppi+fLlGDdunMx61MjISHh6ekJNTQ0AEBoaijFjxmDbtm1wcnKCq6srPv74Y5lA/aZx161bhx49emDhwoUAAEtLSyQnJ2PNmjXw9fUt/2aX4cWLF9izZw9u3LiBsWPHAgAWLFggc/1Zs2Zh7969pcJ6Zd29exdRUVFISEhA586dAQDh4eEwMTFBREQEPvroozLPCwwMxJIlS0q1h2tvgI66crVqIVIED5aukXcJ9Ipmi27KuwSiOkFhZ2jv3LmDS5cuYejQoQAAFRUVDBkyBCEhITL9Xg17jRo1En8U/mpbZmYmAODevXvIz89Hz549xRlRbW1t7Nq1C6mpqTLjOjs7l1tbZmYmHj58iB49epTb5/Tp0+jRoweaNGkCHR0djBw5Eo8ePZJZ/hAZGSmznGDw4MF4+PAhoqKi4OXlhdjYWDg5OcnMrr5p3JSUFDHgl3BxccHdu3fx4sWLcut91ZYtW6CtrQ0NDQ2MGTMG06dPx/jx4wEA+/btg4uLCxo3bgxtbW0sWLAADx48qNS4ZUlJSYGKigo6dOggttWrVw9WVlZISUkp9zx/f39kZ2eLr/T09GrXQERERO83hQ20ISEhKCoqgrGxMVRUVKCiooKtW7fi4MGDyM7OFvupqqqKX0skEpn3JW3FxcUAIK41jY6ORlJSkvhKTk6WWUcLAFpaWuXWpqGhUWHtaWlp8Pb2hr29PQ4ePIirV6/iq6++AgDxR/cZGRm4du1aqd0b1NXV0bNnTyxcuBAXLlyAr68vFi9eXOlxa8Lw4cORlJSE+/fvIy8vD+vWrYOSkhISExMxfPhw9O7dG0eOHMG1a9cwf/58mWsrKSmVWhZS0dKB6pJKpdDV1ZV5ERERUe2kkEsOioqKsGvXLgQHB+ODDz6QOTZgwADs2bMH1tbWVR7X1tYWUqkUDx48kFleUFU6OjowNTVFTEwM3N3dSx2/evUqiouLERwcDCWll/+m2L9/v0yfw4cPo3PnzjA0NHxjzREREZUe18bGptQ2XwkJCbC0tISycuV+FK+npyezHrjEhQsX0Lx5c8yfP19s+/3332X6NGjQAL/88otMW1JSUql/aLxab1FRES5evCguOXj06BHu3LkDW1vbStVLREREtZtCBtojR44gKysLfn5+0NPTkzk2ePBghISEYM2aqq8j09HRwaxZszB9+nQUFxejS5cuyM7ORkJCAnR1dTFq1KhKjxUQEIBx48ahYcOG6NWrF54+fYqEhARMnjwZFhYWKCwsxKZNm9C3b18kJCRg27ZtMueX7G5Q4tGjR/joo4/w2Wefwd7eHjo6Orhy5QpWr16N/v37A0Clxp05cybatWuHZcuWYciQIUhMTMTmzZtrZC/Zli1b4sGDB9i7dy/atWuH6Oho/PjjjzJ9unfvjjVr1mDXrl3o1KkTvv/+e/zyyy9wdHQsd8z+/ftjzJgx2L59O3R0dDBv3jw0adJE/NxERERUtynkkoOQkBB4eHiUCrPAy0B75coV3Lhxo1pjL1u2DAsXLkRgYCBsbGzg5eWF6OhomJmZVWmcUaNGYf369diyZQvs7Ozg7e0tbjXVpk0brFu3DqtWrUKrVq0QHh6OwMBA8dy8vDzExMTIBFptbW106NABX375Jbp164ZWrVph4cKFGDNmDDZv3lypcQHAyckJ+/fvx969e9GqVSssWrQIS5curfIDYWXp168fpk+fjkmTJsHBwQEXLlwQHz4r4enpiYULF2LOnDlo164dnj59ik8++aTCcXfu3Im2bdvC29sbnTp1giAIOHr0aLmzukRERFS3SITXFzSS3B06dAgLFixAcnKyvEupNXJycqCnp4df/G24ywERvTPc5YDovyn5/p2dnV3h8zAKOUNb22lra2PVqlXyLoOIiIhIISjkGtra7vUH3d6V8+fPo1evXuUeL+s3mRERERHJGwMtiZydnZGUlCTvMoiIiIiqhIGWRBoaGmVux0VERET0PuMaWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQuNDYVSnmMz7qcKNmYmIiEjxcIaWiIiIiBQaAy0RERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQuO2XVSndFuwB8pSjbc2/tU1n7y1sYmIiKhsnKElIiIiIoXGQEtERERECo2BloiIiIgUGgMtERERESk0BloiIiIiUmgMtERERESk0BhoiYiIiEihMdASERERkUJjoCUiIiIihcZAW8ulpaVBIpEgKSlJLtcPDQ2Fvr6+XK5NREREdQMDrQJJTEyEsrIy+vTpU+lzTExMkJGRgVatWlX5ekuWLEHTpk0hkUgqfMXGxlZ5bCIiIqKaoiLvAqjyQkJCMHnyZISEhODhw4cwNjZ+4znKyspo3Lhxta4XGRmJdevWoVu3bmLb1KlTkZOTg507d4pthoaG1RqfiIiIqCZwhlZB5ObmYt++fRg/fjz69OmD0NBQ8VhWVhaGDx+OBg0aQENDAy1bthQD5+tLDl68eAE/Pz+YmZlBQ0MDVlZW2LBhQ6nrpaen49atW/Dy8kLjxo3Fl4aGBqRSqfheKpVi9OjRMDAwgKamJnr16oW7d++W+zn+97//wdnZGQMHDkRYWBjq1auHgoICmT4DBgzAyJEjxfdbt25FixYtoKamBisrK3z33Xf/4U4SERFRbcNAqyD2798Pa2trWFlZYcSIEdixYwcEQQAALFy4EMnJyTh27BhSUlKwdetW1K9fv8xxiouL0bRpU/zwww9ITk7GokWL8MUXX2D//v0y/aKiouDm5gZdXd0K6/L19cWVK1cQFRWFxMRECIKA3r17o7CwsFTf9PR0dO3aFa1atcKBAwfg4+ODFy9eICoqSuyTmZmJ6OhofPbZZwCAH3/8EVOnTsXMmTPxyy+/4PPPP8enn36Ks2fPVlhXQUEBcnJyZF5ERERUO3HJgYIICQnBiBEjAABeXl7Izs5GXFwc3Nzc8ODBAzg6OsLZ2RkAYGpqWu44qqqqWLJkifjezMwMiYmJ2L9/P3x8fMT2yMhI9O/fv8Ka7t69i6ioKCQkJKBz584AgPDwcJiYmCAiIgIfffSR2PfOnTvo2bMnBg4ciPXr10MikUBDQwPDhg3Dzp07xb7ff/89mjVrBjc3NwDA2rVr4evriwkTJgAAZsyYgZ9++glr166Fu7t7ubUFBgbKfM4S4doboKOuXOHn+i8eLF3z1sYmIiKqimaLbsq7hHeGM7QK4M6dO7h06RKGDh0KAFBRUcGQIUMQEhICABg/fjz27t0LBwcHzJkzBxcuXKhwvK+++gpt27ZFgwYNoK2tja+//hoPHjwQj+fk5CAuLg79+vWrcJyUlBSoqKigQ4cOYlu9evVgZWWFlJQUse3ff/9F165dMWjQIGzYsAESiUQ8NmbMGJw8eRJ//vkngJe7Ivj6+op9UlJS4OLiInNdFxcXmfHL4u/vj+zsbPGVnp5eYX8iIiJSXAy0CiAkJARFRUUwNjaGiooKVFRUsHXrVhw8eBDZ2dno1asXfv/9d0yfPh0PHz5Ejx49MGvWrDLH2rt3L2bNmgU/Pz+cPHkSSUlJ+PTTT/H8+XOxz7Fjx2BrawsTE5MaqV8qlcLDwwNHjhwRg2sJR0dHtGnTBrt27cLVq1dx69Yt+Pr61sg1dXV1ZV5ERERUOzHQvueKioqwa9cuBAcHIykpSXxdv34dxsbG2LNnDwCgQYMGGDVqFL7//nusX78eX3/9dZnjlSwPmDBhAhwdHWFhYYHU1FSZPpVZbgAANjY2KCoqwsWLF8W2R48e4c6dO7C1tRXblJSU8N1336Ft27Zwd3fHw4cPZcYZPXo0QkNDsXPnTnh4eMgEaRsbGyQkJJT6DK+OT0RERHUb19C+544cOYKsrCz4+flBT09P5tjgwYPFLbzatm0LOzs7FBQU4MiRI7CxsSlzvJYtW2LXrl04ceIEzMzM8N133+Hy5cswMzMD8DJAHzt2rNwZ3tfH6t+/P8aMGYPt27dDR0cH8+bNQ5MmTUoFYmVlZYSHh2Po0KHo3r07YmNjxe3Ehg0bhlmzZuGbb77Brl27ZM6bPXs2fHx84OjoCA8PDxw+fBiHDh3C6dOnK30PiYiIqHbjDO17LiQkBB4eHqXCLPAy0F65cgUqKirw9/eHvb09unXrBmVlZezdu7fM8T7//HMMGjQIQ4YMQYcOHfDo0SPxgSsAiIuLg7a2NpycnCpV386dO9G2bVt4e3ujU6dOEAQBR48ehaqqaqm+Kioq2LNnD+zs7NC9e3dkZmYCAPT09DB48GBoa2tjwIABMucMGDAAGzZswNq1a2FnZ4ft27dj586d4kNjRERERBKhZO8nIgBTpkxBUVERtmzZ8k6v26NHD9jZ2WHjxo1vZfycnBzo6enhF3+bt7rLARER0fuiNuxyUPL9Ozs7u8LnYbjkgGS0atUKnTp1emfXy8rKQmxsLGJjY995iCYiIqLagYGWZIwdO/adXs/R0RFZWVlYtWoVrKys3um1iYiIqHZgoCW5SktLk3cJREREpOD4UBgRERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQuMuB1SnmMz7qcKNmYmIiEjxcIaWiIiIiBQaAy0RERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQuO2XVSndFuwB8pSDXmXQUQK6uqaT+RdAhGVgTO0RERERKTQGGiJiIiISKEx0BIRERGRQmOgJSIiIiKFxkBLRERERAqNgZaIiIiIFBoDLREREREpNAZaIiIiIlJoDLREREREpNAYaOmdCggIgIODg7zLICIiolqEgbaSfH19IZFISr28vLzkXVqVuLu749tvv0VaWhokEgmSkpLkXRIRERHRf6Ii7wIUiZeXF3bu3CnTJpVKqzWWIAh48eIFVFTe3R/B48ePkZCQgL179+Lff/99Z9clIiIieps4Q1sFUqkUjRs3lnkZGBiUOdv55MkTSCQSxMbGAgBiY2MhkUhw7NgxtG3bFlKpFPHx8SgoKMCUKVPQsGFDqKuro0uXLrh8+bI4Tsl50dHRsLe3h7q6Ojp27IhffvlFprb4+Hh07doVGhoaMDExwZQpU5CXlyfTJzo6Gk5OTmjUqNEbP2tl64qJiYGzszM0NTXRuXNn3LlzR2acoKAgNGrUCDo6OvDz88OzZ89kjhcXF2Pp0qVo2rQppFIpHBwccPz4cfF4yb09dOgQ3N3doampiTZt2iAxMfGNn4GIiIjqBgbad2zevHkICgpCSkoK7O3tMWfOHBw8eBBhYWH4+eefYWFhAU9PTzx+/FjmvNmzZyM4OBiXL19GgwYN0LdvXxQWFgIAUlNT4eXlhcGDB+PGjRvYt28f4uPjMWnSJJkxoqKi0L9//0rVWdm65s+fj+DgYFy5cgUqKir47LPPxGP79+9HQEAAVq5ciStXrsDIyAhbtmyROX/Dhg0IDg7G2rVrcePGDXh6eqJfv364e/duqevMmjULSUlJsLS0xNChQ1FUVFRu/QUFBcjJyZF5ERERUe0kEQRBkHcRisDX1xfff/891NXVZdq/+OILDBs2DGZmZrh27Zr4wNOTJ09gYGCAs2fPws3NDbGxsXB3d0dERIQYKvPy8mBgYIDQ0FAMGzYMAFBYWAhTU1NMmzYNs2fPFs/bu3cvhgwZAuDl0oGmTZsiNDQUPj4+GD16NJSVlbF9+3axrvj4eLi6uiIvLw/q6uooKChA/fr18dNPP8HOzg5paWmlai5RlbpOnz6NHj16AACOHj2KPn364N9//4W6ujo6d+4MR0dHfPXVV+LYHTt2xLNnz8TZ7CZNmmDixIn44osvxD7t27dHu3bt8NVXX4l1fvvtt/Dz8wMAJCcnw87ODikpKbC2ti7zzysgIABLliwp1f6Lvw101JXL/4MmqkOaLbop7xKIiCqUk5MDPT09ZGdnQ1dXt9x+nKGtAnd3dyQlJcm8xo0bV6UxnJ2dxa9TU1NRWFgIFxcXsU1VVRXt27dHSkqKzHmdOnUSvzY0NISVlZXY5/r16wgNDYW2trb48vT0RHFxMe7fvw8AOHPmDBo2bAg7O7s31liVuuzt7cWvjYyMAACZmZkAgJSUFHTo0KHcz5GTk4OHDx/KXAcAXFxcqnSdsvj7+yM7O1t8paenl/+BiYiISKHxobAq0NLSgoWFRan23NxcAC8f9CpRshygrDFqWm5uLj7//HNMmTKl1LFmzZoBeLncoF+/fjV+bVVVVfFriUQC4OW6WHlfRyqVVvuBPSIiIlIsnKGtAQ0aNAAAZGRkiG2V2Q6rRYsWUFNTQ0JCgthWWFiIy5cvw9bWVqbvTz/9JH6dlZWFX3/9FTY2NgAAJycnJCcnw8LCotRLTU0NgiDg8OHDlV4/W5W6KmJjY4OLFy+W+zl0dXVhbGwscx0ASEhIqNJ1iIiIqG7jDG0VFBQU4K+//pJpU1FRQf369dGxY0cEBQXBzMwMmZmZWLBgwRvH09LSwvjx4zF79mwYGhqiWbNmWL16NfLz88X1oiWWLl2KevXqoVGjRpg/fz7q16+PAQMGAADmzp2Ljh07YtKkSRg9ejS0tLSQnJyMU6dOYfPmzbh69Sry8/PRpUuXUjW8visBANjZ2VW6ropMnToVvr6+cHZ2houLC8LDw3Hr1i2Ym5uLfWbPno3FixejRYsWcHBwwM6dO5GUlITw8PBKX4eIiIjqNgbaKjh+/Li4frOElZUVbt++jR07dsDPzw9t27aFlZUVVq9ejQ8++OCNYwYFBaG4uBgjR47E06dP4ezsjBMnTsDAwKBUv6lTp+Lu3btwcHDA4cOHoaamBuDl+tK4uDjMnz8fXbt2hSAIaNGihfgQWWRkJHr37l3mnrcff/xxqbb09PRK11WRIUOGIDU1FXPmzMGzZ88wePBgjB8/HidOnBD7TJkyBdnZ2Zg5cyYyMzNha2uLqKgotGzZstLXISIiorqNuxy850p2E8jKyoK+vn61xrC3t8eCBQvg4+NTs8UpkJKnJLnLAdH/x10OiOh9x10OCADw/PlzDB48GL169ZJ3KURERERvBZcc1HJqampYvHixvMsgIiIiemsYaN9zbm5u4KoQIiIiovJxyQERERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQuMuB1SnmMz7qcKNmYmIiEjxcIaWiIiIiBQaAy0RERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQuO2XVSndFuwB8pSDXmXQVSrXF3zibxLIKI6jjO0RERERKTQGGiJiIiISKEx0BIRERGRQmOgJSIiIiKFxkBLRERERAqNgZaIiIiIFBoDLREREREpNAZaIiIiIlJoDLREREREpNAYaOmd8fX1xYABA97YTyKRICIiotzjaWlpkEgkSEpKqrHaiIiISHEx0FaDr68vJBJJqZeXl5e8S6sSd3d3fPvtt2JAVFZWxp9//inTJyMjAyoqKpBIJEhLS3sndWVkZKBXr17v5FpERESk+Bhoq8nLywsZGRkyrz179lRrLEEQUFRUVMMVVuzx48dISEhA3759xbYmTZpg165dMv3CwsLQpEmTd1LT8+fPAQCNGzeGVCp9J9ckIiIixcdAW01SqRSNGzeWeRkYGJT54/AnT55AIpEgNjYWABAbGwuJRIJjx46hbdu2kEqliI+PR0FBAaZMmYKGDRtCXV0dXbp0weXLl8VxSs6Ljo6Gvb091NXV0bFjR/zyyy8ytcXHx6Nr167Q0NCAiYkJpkyZgry8PJk+0dHRcHJyQqNGjcS2UaNGYefOnTL9du7ciVGjRsm0vXjxAn5+fjAzM4OGhgasrKywYcOGUn1mzJgBfX191KtXD3PmzIEgCDJ93NzcMGnSJEybNg3169eHp6cngNJLDi5dugRHR0eoq6vD2dkZ165dq+BPhoiIiOoaBlo5mjdvHoKCgpCSkgJ7e3vMmTMHBw8eRFhYGH7++WdYWFjA09MTjx8/ljlv9uzZCA4OxuXLl9GgQQP07dsXhYWFAIDU1FR4eXlh8ODBuHHjBvbt24f4+HhMmjRJZoyoqCj0799fpq1fv37IyspCfHw8gJfBOCsrS2YWFwCKi4vRtGlT/PDDD0hOTsaiRYvwxRdfYP/+/WKf4OBghIaGYseOHYiPj8fjx4/x448/lroHYWFhUFNTQ0JCArZt21bqeG5uLry9vWFra4urV68iICAAs2bNeuO9LSgoQE5OjsyLiIiIaicVeRegqI4cOQJtbW2Zti+++ALDhg2r9BhLly5Fz549AQB5eXnYunUrQkNDxfWj33zzDU6dOoWQkBDMnj1bPG/x4sXieWFhYWjatCl+/PFH+Pj4IDAwEMOHD8e0adMAAC1btsTGjRvh6uqKrVu3Ql1dHQUFBTh+/DgCAgJk6lFVVcWIESOwY8cOdOnSBTt27MCIESOgqqpaqt+SJUvE92ZmZkhMTMT+/fvh4+MDAFi/fj38/f0xaNAgAMC2bdtw4sSJUvegZcuWWL16dbn3aPfu3SguLkZISAjU1dVhZ2eHP/74A+PHj6/w3gYGBsrUWCJcewN01JUrPJeIqubB0jXyLuGNmi26Ke8SiOgtYqCtJnd3d2zdulWmzdDQsEozgc7OzuLXqampKCwshIuLi9imqqqK9u3bIyUlRea8Tp06yVzTyspK7HP9+nXcuHED4eHhYh9BEFBcXIz79+/DxsYGZ86cQcOGDWFnZ1eqps8++wydO3fGypUr8cMPPyAxMbHM9b1fffUVduzYgQcPHuDff//F8+fP4eDgAADIzs5GRkYGOnToIPZXUVGBs7NzqWUHbdu2rfAelcxeq6url/n5y+Pv748ZM2aI73NycmBiYvLG84iIiEjxMNBWk5aWFiwsLEq15+bmAoBMcCtZDlDWGDUtNzcXn3/+OaZMmVLqWLNmzQC8XG7Qr1+/Ms9v3bo1rK2tMXToUNjY2KBVq1altsfau3cvZs2aheDgYHTq1Ak6OjpYs2YNLl68WOV638Y9AF6uceaDZURERHUD19DWsAYNGgB4ufVUicrsl9qiRQtxLWmJwsJCXL58Gba2tjJ9f/rpJ/HrrKws/Prrr7CxsQEAODk5ITk5GRYWFqVeampqEAQBhw8fLrV+9lWfffYZYmNj8dlnn5V5PCEhAZ07d8aECRPg6OgICwsLpKamisf19PRgZGQkE3CLiopw9erVN96H19nY2ODGjRt49uxZmZ+fiIiIiDO01VRQUIC//vpLpk1FRQX169dHx44dERQUBDMzM2RmZmLBggVvHE9LSwvjx4/H7NmzYWhoiGbNmmH16tXIz8+Hn5+fTN+lS5eiXr16aNSoEebPn4/69euLv7Bg7ty56NixIyZNmoTRo0dDS0sLycnJOHXqFDZv3oyrV68iPz8fXbp0KbeWMWPG4KOPPoK+vn6Zx1u2bIldu3bhxIkTMDMzw3fffYfLly/DzMxM7DN16lQEBQWhZcuWsLa2xrp16/DkyZM33ofXDRs2DPPnz8eYMWPg7++PtLQ0rF27tsrjEBERUe3FGdpqOn78OIyMjGReJSFxx44dKCoqQtu2bTFt2jQsX768UmMGBQVh8ODBGDlyJJycnHDv3j2cOHECBgYGpfpNnToVbdu2xV9//YXDhw9DTU0NAGBvb4+4uDj8+uuv6Nq1KxwdHbFo0SIYGxsDACIjI9G7d2+oqJT/b5mSYF5en88//xyDBg3CkCFD0KFDBzx69AgTJkyQ6TNz5kyMHDkSo0aNEpclDBw4sFL34VXa2to4fPgwbt68CUdHR8yfPx+rVq2q8jhERERUe0mE15/SofdWbGws3N3dkZWVVe7s6ZvY29tjwYIF4m4EdUVOTg709PTwi78NdzkgqoO4ywGRYir5/p2dnQ1dXd1y+3GGtg55/vw5Bg8ezF8rS0RERLUK19DWIWpqali8eLG8yyAiIiKqUQy0CsTNza3UPq5EREREdR2XHBARERGRQmOgJSIiIiKFxkBLRERERAqNgZaIiIiIFBoDLREREREpNO5yQHWKybyfKtyYmYiIiBQPZ2iJiIiISKEx0BIRERGRQmOgJSIiIiKFxkBLRERERAqNgZaIiIiIFBoDLREREREpNAZaIiIiIlJoDLREREREpNAYaImIiIhIoTHQEhEREZFCY6AlIiIiIoXGQEtERERECo2BloiIiIgUGgMtERERESk0BloiIiIiUmgMtERERESk0Bho3yE3NzdMmzZNfJ+fn4/BgwdDV1cXEokET548gampKdavXy+3Gmuar68vBgwYUKVzJBIJIiIi3ko9REREVPsw0L7G19cXEokEEokEampqsLCwwNKlS1FUVPSfxz506BCWLVsmvg8LC8P58+dx4cIFZGRkQE9PD5cvX8bYsWOrNG5cXBxMTEzE+ssKkLGxsWJoJiIiIqpNVORdwPvIy8sLO3fuREFBAY4ePYqJEydCVVUV/v7+/2lcQ0NDmfepqamwsbFBq1atxLYGDRpUedzIyEj07dv3P9VGREREpKg4Q1sGqVSKxo0bo3nz5hg/fjw8PDwQFRWFdevWoXXr1tDS0oKJiQkmTJiA3NxcmXMTEhLg5uYGTU1NGBgYwNPTE1lZWQBklxy4ubkhODgY586dg0QigZubGwCUWnLw5MkTfP7552jUqBHU1dXRqlUrHDlyROaaUVFR6NevX5U/58GDB2FnZwepVApTU1MEBwfLHDc1NcXKlSvx2WefQUdHB82aNcPXX38t0+fmzZvo3r07NDQ0UK9ePYwdO7bUPXl9zNeXVDg4OCAgIKDM/mXNLCclJUEikSAtLa0qH5eIiIhqKQbaStDQ0MDz58+hpKSEjRs34tatWwgLC8OZM2cwZ84csV9SUhJ69OgBW1tbJCYmIj4+Hn379sWLFy9KjXno0CGMGTMGnTp1QkZGBg4dOlSqT3FxMXr16oWEhAR8//33SE5ORlBQEJSVlcU+t27dQmZmJrp3716lz3T16lX4+Pjg448/xs2bNxEQEICFCxciNDRUpl9wcDCcnZ1x7do1TJgwAePHj8edO3cAAHl5efD09ISBgQEuX76MH374AadPn8akSZOqVMvbUFBQgJycHJkXERER1U5cclABQRAQExODEydOYPLkyTIPdJmammL58uUYN24ctmzZAgBYvXo1nJ2dxfcAYGdnV+bYhoaG0NTUhJqaGho3blxmn9OnT+PSpUtISUmBpaUlAMDc3FymT2RkJDw9PaGmpia2HTlyBNra2jL9Xg/V69atQ48ePbBw4UIAgKWlJZKTk7FmzRr4+vqK/Xr37o0JEyYAAObOnYsvv/wSZ8+ehZWVFXbv3o1nz55h165d0NLSAgBs3rwZffv2xapVq9CoUaMyP9e7EBgYiCVLlpRqTw/qCB115TLOICIioupotuimvEvgDG1ZSgKhuro6evXqhSFDhiAgIACnT59Gjx490KRJE+jo6GDkyJF49OgR8vPzAfz/GdqakpSUhKZNm4phtiyRkZGllhu4u7sjKSlJ5vXtt9/K9ElJSYGLi4tMm4uLC+7evSsTfu3t7cWvJRIJGjdujMzMTHGMNm3aiGG2ZIzi4mJxFlde/P39kZ2dLb7S09PlWg8RERG9PZyhLYO7uzu2bt0KNTU1GBsbQ0VFBWlpafD29sb48eOxYsUKGBoaIj4+Hn5+fnj+/Dk0NTWhoaFRo3W8abyMjAxcu3YNffr0kWnX0tKChYWFTNsff/xRrRpUVVVl3kskEhQXF1drLABQUlKCIAgybYWFhRX2ByBzTkX9S0ilUkil0mpWSURERIqEM7RlKAmEzZo1g4rKy8x/9epVFBcXIzg4GB07doSlpSUePnwoc569vT1iYmJqrA57e3v88ccf+PXXX8s8fvjwYXTu3LnU7gmVYWNjg4SEBJm2hIQEWFpayqzRfdMY169fR15enswYSkpKsLKyKvOcBg0aICMjQ3yfk5OD+/fvl3uNkl0fXj0nKSmpUvURERFR3cBAW0kWFhYoLCzEpk2b8Ntvv+G7777Dtm3bZPr4+/vj8uXLmDBhAm7cuIHbt29j69at+Oeff6p1TVdXV3Tr1g2DBw/GqVOncP/+fRw7dgzHjx8HUP3dDQBg5syZiImJwbJly/Drr78iLCwMmzdvxqxZsyo9xvDhw6Guro5Ro0bhl19+wdmzZzF58mSMHDmy3PWz3bt3x3fffYfz58/j5s2bGDVqVIUB2sLCAiYmJggICMDdu3cRHR1dajcGIiIiqtsYaCupTZs2WLduHVatWoVWrVohPDwcgYGBMn0sLS1x8uRJXL9+He3bt0enTp0QGRkpzvJWx8GDB9GuXTsMHToUtra2mDNnDl68eIG8vDzExMRUO9A6OTlh//792Lt3L1q1aoVFixZh6dKlMg+EvYmmpiZOnDiBx48fo127dvjwww/Ro0cPbN68udxz/P394erqCm9vb/Tp0wcDBgxAixYtyu2vqqqKPXv24Pbt27C3t8eqVauwfPnyqnxUIiIiquUkwusLGkkhHDp0CAsWLEBycrK8S1EIOTk50NPTwy/+NtzlgIiIqAa9zV0OSr5/Z2dnQ1dXt9x+nKFVUNra2li1apW8yyAiIiKSO+5yoKA++OADeZdARERE9F7gDC0RERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQmOgJSIiIiKFxm27qE4xmfdThRszExERkeLhDC0RERERKTQGWiIiIiJSaFxyQHWCIAgAXv5OaCIiIlIMJd+3S76Pl4eBluqER48eAQBMTEzkXAkRERFV1dOnT6Gnp1fucQZaqhMMDQ0BAA8ePKjwPwiqWTk5OTAxMUF6ejofxnuHeN/lh/dePnjf5edt33tBEPD06VMYGxtX2I+BluoEJaWXy8X19PT4f3ZyoKury/suB7zv8sN7Lx+87/LzNu99ZSai+FAYERERESk0BloiIiIiUmgMtFQnSKVSLF68GFKpVN6l1Cm87/LB+y4/vPfywfsuP+/LvZcIb9oHgYiIiIjoPcYZWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0FKt8NVXX8HU1BTq6uro0KEDLl26VGH/H374AdbW1lBXV0fr1q1x9OjRd1Rp7VOVe//NN9+ga9euMDAwgIGBATw8PN74Z0Vlq+rf+RJ79+6FRCLBgAED3m6BtVhV7/2TJ08wceJEGBkZQSqVwtLSkv+fUw1Vve/r16+HlZUVNDQ0YGJigunTp+PZs2fvqNra4dy5c+jbty+MjY0hkUgQERHxxnNiY2Ph5OQEqVQKCwsLhIaGvvU6AQACkYLbu3evoKamJuzYsUO4deuWMGbMGEFfX1/4+++/y+yfkJAgKCsrC6tXrxaSk5OFBQsWCKqqqsLNmzffceWKr6r3ftiwYcJXX30lXLt2TUhJSRF8fX0FPT094Y8//njHlSu2qt73Evfv3xeaNGkidO3aVejfv/+7KbaWqeq9LygoEJydnYXevXsL8fHxwv3794XY2FghKSnpHVeu2Kp638PDwwWpVCqEh4cL9+/fF06cOCEYGRkJ06dPf8eVK7ajR48K8+fPFw4dOiQAEH788ccK+//222+CpqamMGPGDCE5OVnYtGmToKysLBw/fvyt18pASwqvffv2wsSJE8X3L168EIyNjYXAwMAy+/v4+Ah9+vSRaevQoYPw+eefv9U6a6Oq3vvXFRUVCTo6OkJYWNjbKrFWqs59LyoqEjp37ix8++23wqhRoxhoq6mq937r1q2Cubm58Pz583dVYq1U1fs+ceJEoXv37jJtM2bMEFxcXN5qnbVZZQLtnDlzBDs7O5m2IUOGCJ6enm+xspe45IAU2vPnz3H16lV4eHiIbUpKSvDw8EBiYmKZ5yQmJsr0BwBPT89y+1PZqnPvX5efn4/CwkIYGhq+rTJrnere96VLl6Jhw4bw8/N7F2XWStW591FRUejUqRMmTpyIRo0aoVWrVli5ciVevHjxrspWeNW57507d8bVq1fFZQm//fYbjh49it69e7+TmusqeX5/VXnrVyB6i/755x+8ePECjRo1kmlv1KgRbt++XeY5f/31V5n9//rrr7dWZ21UnXv/urlz58LY2LjU/wFS+apz3+Pj4xESEoKkpKR3UGHtVZ17/9tvv+HMmTMYPnw4jh49inv37mHChAkoLCzE4sWL30XZCq86933YsGH4559/0KVLFwiCgKKiIowbNw5ffPHFuyi5zirv+2tOTg7+/fdfaGhovLVrc4aWiOQiKCgIe/fuxY8//gh1dXV5l1NrPX36FCNHjsQ333yD+vXry7ucOqe4uBgNGzbE119/jbZt22LIkCGYP38+tm3bJu/SarXY2FisXLkSW7Zswc8//4xDhw4hOjoay5Ytk3dp9JZwhpYUWv369aGsrIy///5bpv3vv/9G48aNyzyncePGVepPZavOvS+xdu1aBAUF4fTp07C3t3+bZdY6Vb3vqampSEtLQ9++fcW24uJiAICKigru3LmDFi1avN2ia4nq/J03MjKCqqoqlJWVxTYbGxv89ddfeP78OdTU1N5qzbVBde77woULMXLkSIwePRoA0Lp1a+Tl5WHs2LGYP38+lJQ4n/c2lPf9VVdX963OzgKcoSUFp6amhrZt2yImJkZsKy4uRkxMDDp16lTmOZ06dZLpDwCnTp0qtz+VrTr3HgBWr16NZcuW4fjx43B2dn4XpdYqVb3v1tbWuHnzJpKSksRXv3794O7ujqSkJJiYmLzL8hVadf7Ou7i44N69e+I/IgDg119/hZGREcNsJVXnvufn55cKrSX/qBAE4e0VW8fJ9fvrW3/sjOgt27t3ryCVSoXQ0FAhOTlZGDt2rKCvry/89ddfgiAIwsiRI4V58+aJ/RMSEgQVFRVh7dq1QkpKirB48WJu21VNVb33QUFBgpqamnDgwAEhIyNDfD19+lReH0EhVfW+v467HFRfVe/9gwcPBB0dHWHSpEnCnTt3hCNHjggNGzYUli9fLq+PoJCqet8XL14s6OjoCHv27BF+++034eTJk0KLFi0EHx8feX0EhfT06VPh2rVrwrVr1wQAwrp164Rr164Jv//+uyAIgjBv3jxh5MiRYv+Sbbtmz54tpKSkCF999RW37SKqik2bNgnNmjUT1NTUhPbt2ws//fSTeMzV1VUYNWqUTP/9+/cLlpaWgpqammBnZydER0e/44prj6rc++bNmwsASr0WL1787gtXcFX9O/8qBtr/pqr3/sKFC0KHDh0EqVQqmJubCytWrBCKiorecdWKryr3vbCwUAgICBBatGghqKurCyYmJsKECROErKysd1+4Ajt79myZ/59dcq9HjRoluLq6ljrHwcFBUFNTE8zNzYWdO3e+k1olgsC5dyIiIiJSXFxDS0REREQKjYGWiIiIiBQaAy0RERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQmOgJSIiIiKFxkBLRES1ikQiQUREhLzLKNP7XBuRImOgJSKiOiktLQ0SiQRJSUnv5DwiensYaImI6K15/vy5vEsgojqAgZaIiERubm6YMmUK5syZA0NDQzRu3BgBAQHi8QcPHqB///7Q1taGrq4ufHx88Pfff4vHAwIC4ODggG+//RZmZmZQV1cH8PJH7du3b4e3tzc0NTVhY2ODxMRE3Lt3D25ubtDS0kLnzp2RmpoqU09kZCScnJygrq4Oc3NzLFmyBEVFReLxu3fvolu3blBXV4etrS1OnTpV6c9qZmYGAHB0dIREIoGbmxsAoLi4GEuXLkXTpk0hlUrh4OCA48ePv/G8y5cvo2fPnqhfvz709PTg6uqKn3/+udL1EFH1MdASEZGMsLAwaGlp4eLFi1i9ejWWLl2KU6dOobi4GP3798fjx48RFxeHU6dO4bfffsOQIUNkzr937x4OHjyIQ4cOyfxYftmyZfjkk0+QlJQEa2trDBs2DJ9//jn8/f1x5coVCIKASZMmif3Pnz+PTz75BFOnTkVycjK2b9+O0NBQrFixAsDL4Dlo0CCoqanh4sWL2LZtG+bOnVvpz3np0iUAwOnTp5GRkYFDhw4BADZs2IDg4GCsXbsWN27cgKenJ/r164e7d+9WeN7Tp08xatQoxMfH46effkLLli3Ru3dvPH36tIp/AkRUZQIREdH/cXV1Fbp06SLT1q5dO2Hu3LnCyZMnBWVlZeHBgwfisVu3bgkAhEuXLgmCIAiLFy8WVFVVhczMTJkxAAgLFiwQ3ycmJgoAhJCQELFtz549grq6uvi+R48ewsqVK2XG+e677wQjIyNBEAThxIkTgoqKivDnn3+Kx48dOyYAEH788cc3ftb79+8LAIRr167JtBsbGwsrVqwodQ8mTJhQ4Xmve/HihaCjoyMcPnxYbKtsbURUNZyhJSIiGfb29jLvjYyMkJmZiZSUFJiYmMDExEQ8ZmtrC319faSkpIhtzZs3R4MGDSoct1GjRgCA1q1by7Q9e/YMOTk5AIDr169j6dKl0NbWFl9jxoxBRkYG8vPzxXqMjY3FMTp16vSfPntOTg4ePnwIFxcXmXYXFxeZz1iWv//+G2PGjEHLli2hp6cHXV1d5Obm4sGDB/+pJiJ6MxV5F0BERO8XVVVVmfcSiQTFxcWVPl9LS+uN40okknLbSq6Vm5uLJUuWYNCgQaXGKlmb+z4ZNWoUHj16hA0bNqB58+aQSqXo1KkTH4wjegcYaImIqFJsbGyQnp6O9PR0cZY2OTkZT548ga2tbY1fz8nJCXfu3IGFhUWF9WRkZMDIyAgA8NNPP1V6fDU1NQDAixcvxDZdXV0YGxsjISEBrq6uYntCQgLat29f7nklfbZs2YLevXsDANLT0/HPP/9Uuh4iqj4GWiIiqhQPDw+0bt0aw4cPx/r161FUVIQJEybA1dUVzs7ONX69RYsWwdvbG82aNcOHH34IJSUlXL9+Hb/88guWL18ODw8PWFpaYtSoUVizZg1ycnIwf/78So/fsGFDaGho4Pjx42jatCnU1dWhp6eH2bNnY/HixWjRogUcHBywc+dOJCUlITw8vMLzWrZsie+++w7Ozs7IycnB7NmzoaGhUeP3hYhK4xpaIiKqFIlEgsjISBgYGKBbt27w8PCAubk59u3b91au5+npiSNHjuDkyZNo164dOnbsiC+//BLNmzcHACgpKeHHH3/Ev//+i/bt22P06NHiDgiVoaKigo0bN2L79u0wNjZG//79AQBTpkzBjBkzMHPmTLRu3RrHjx9HVFQUWrZsWeF5ISEhyMrKgpOTE0aOHIkpU6agYcOGNXxXiKgsEkEQBHkXQURERERUXZyhJSIiIiKFxkBLRES10sqVK2W2/Hr11atXL3mXR0Q1iEsOiIioVnr8+DEeP35c5jENDQ00adLkHVdERG8LAy0RERERKTQuOSAiIiIihcZAS0REREQKjYGWiIiIiBQaAy0RERERKTQGWiIiIiJSaAy0RERERKTQGGiJiIiISKEx0BIRERGRQvt/UExd1jNmJ2sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='normed_total',y='tz',hue='os',data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8b2c39df-17f1-4b84-b8cd-ed98d4301e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = count_subset.groupby('tz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ba978970-03d8-4887-8dee-2d16c3838eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "result2 = count_subset['total']/g['total'].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4f79e2aa-1629-47ae-b470-4e9d96a86a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "unames = [\"user_id\", \"gender\", \"age\", \"occupation\", \"zip\"]\n",
    "users = pd.read_table('datasets/movielens/users.dat',sep='::',header=None,names=unames,engine='python')\n",
    "\n",
    "rnames = [\"user_id\", \"movie_id\", \"rating\", \"timestamp\"]\n",
    "ratings = pd.read_table('datasets/movielens/ratings.dat',sep='::',header=None,names=rnames,engine='python')\n",
    "\n",
    "mnames = [\"movie_id\", \"title\", \"genres\"]\n",
    "movies = pd.read_table('datasets/movielens/movies.dat',sep='::',header=None,names=mnames,engine='python')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "92851ded-7ecd-4aa3-9ea7-8090bbb2a240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
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       "   user_id gender  age  occupation    zip\n",
       "0        1      F    1          10  48067\n",
       "1        2      M   56          16  70072\n",
       "2        3      M   25          15  55117\n",
       "3        4      M   45           7  02460\n",
       "4        5      M   25          20  55455"
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       "   movie_id                               title                        genres\n",
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  {
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   "execution_count": 65,
   "id": "8f6b04af-3b57-42a2-9525-b23f8ea8cfad",
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    "data = pd.merge(pd.merge(ratings,users),movies)"
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   "execution_count": 66,
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       "         user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
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       "1              2      1193       5  978298413      M   56          16  70072   \n",
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       "\n",
       "                                               title                genres  \n",
       "0             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "1             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "2             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "3             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
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       "...                                              ...                   ...  \n",
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       "1000206                            White Boys (1999)                 Drama  \n",
       "1000207                     One Little Indian (1973)  Comedy|Drama|Western  \n",
       "1000208  Five Wives, Three Secretaries and Me (1998)           Documentary  \n",
       "\n",
       "[1000209 rows x 10 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
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       "user_id                                            1\n",
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       "occupation                                        10\n",
       "zip                                            48067\n",
       "title         One Flew Over the Cuckoo's Nest (1975)\n",
       "genres                                         Drama\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eafc9ba0-c220-4975-9d7c-2076a1bffe76",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings = data.pivot_table('rating',index='title',\n",
    "                               columns='gender',aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aa65ceda-2415-4e93-b95b-4f12c09a916c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>2.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.675676</td>\n",
       "      <td>2.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.828571</td>\n",
       "      <td>3.689024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "gender                                F         M\n",
       "title                                            \n",
       "$1,000,000 Duck (1971)         3.375000  2.761905\n",
       "'Night Mother (1986)           3.388889  3.352941\n",
       "'Til There Was You (1997)      2.675676  2.733333\n",
       "'burbs, The (1989)             2.793478  2.962085\n",
       "...And Justice for All (1979)  3.828571  3.689024"
      ]
     },
     "execution_count": 69,
     "metadata": {},
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    }
   ],
   "source": [
    "mean_ratings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c724e5c4-6055-4e77-8aef-e1d18a652ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_by_title = data.groupby('title').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ab2aa06c-d474-47ec-97ef-2514cd89a8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "$1,000,000 Duck (1971)            37\n",
       "'Night Mother (1986)              70\n",
       "'Til There Was You (1997)         52\n",
       "'burbs, The (1989)               303\n",
       "...And Justice for All (1979)    199\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d3903384-2975-4f69-b0cb-acf42592984f",
   "metadata": {},
   "outputs": [],
   "source": [
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b3b91da1-0ecf-4c08-81c5-be5adbdd0ff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([''burbs, The (1989)', '10 Things I Hate About You (1999)',\n",
       "       '101 Dalmatians (1961)', '101 Dalmatians (1996)', '12 Angry Men (1957)',\n",
       "       '13th Warrior, The (1999)', '2 Days in the Valley (1996)',\n",
       "       '20,000 Leagues Under the Sea (1954)', '2001: A Space Odyssey (1968)',\n",
       "       '2010 (1984)',\n",
       "       ...\n",
       "       'X-Men (2000)', 'Year of Living Dangerously (1982)',\n",
       "       'Yellow Submarine (1968)', 'You've Got Mail (1998)',\n",
       "       'Young Frankenstein (1974)', 'Young Guns (1988)',\n",
       "       'Young Guns II (1990)', 'Young Sherlock Holmes (1985)',\n",
       "       'Zero Effect (1998)', 'eXistenZ (1999)'],\n",
       "      dtype='object', name='title', length=1216)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a12cb849-627b-4021-94a2-6969c13bbc41",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings = mean_ratings.loc[active_titles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2b44b357-051c-4e6f-af0d-4268d13f9eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Things I Hate About You (1999)</th>\n",
       "      <td>3.646552</td>\n",
       "      <td>3.311966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1961)</th>\n",
       "      <td>3.791444</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.240000</td>\n",
       "      <td>2.911215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.184397</td>\n",
       "      <td>4.328421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns (1988)</th>\n",
       "      <td>3.371795</td>\n",
       "      <td>3.425620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <td>2.934783</td>\n",
       "      <td>2.904025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Sherlock Holmes (1985)</th>\n",
       "      <td>3.514706</td>\n",
       "      <td>3.363344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Effect (1998)</th>\n",
       "      <td>3.864407</td>\n",
       "      <td>3.723140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <td>3.098592</td>\n",
       "      <td>3.289086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1216 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                    F         M\n",
       "title                                                \n",
       "'burbs, The (1989)                 2.793478  2.962085\n",
       "10 Things I Hate About You (1999)  3.646552  3.311966\n",
       "101 Dalmatians (1961)              3.791444  3.500000\n",
       "101 Dalmatians (1996)              3.240000  2.911215\n",
       "12 Angry Men (1957)                4.184397  4.328421\n",
       "...                                     ...       ...\n",
       "Young Guns (1988)                  3.371795  3.425620\n",
       "Young Guns II (1990)               2.934783  2.904025\n",
       "Young Sherlock Holmes (1985)       3.514706  3.363344\n",
       "Zero Effect (1998)                 3.864407  3.723140\n",
       "eXistenZ (1999)                    3.098592  3.289086\n",
       "\n",
       "[1216 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings"
   ]
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  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "233bde8e-a456-4f53-85c8-9c3937ba7bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_female_ratings = mean_ratings.sort_values('F',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "16f65d53-64e2-4011-a46b-8719a3329335",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>4.644444</td>\n",
       "      <td>4.473795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>4.588235</td>\n",
       "      <td>4.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
       "      <td>4.572650</td>\n",
       "      <td>4.464589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallace &amp; Gromit: The Best of Aardman Animation (1996)</th>\n",
       "      <td>4.563107</td>\n",
       "      <td>4.385075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>4.562602</td>\n",
       "      <td>4.491415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "gender                                                     F         M\n",
       "title                                                                 \n",
       "Close Shave, A (1995)                               4.644444  4.473795\n",
       "Wrong Trousers, The (1993)                          4.588235  4.478261\n",
       "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)       4.572650  4.464589\n",
       "Wallace & Gromit: The Best of Aardman Animation...  4.563107  4.385075\n",
       "Schindler's List (1993)                             4.562602  4.491415"
      ]
     },
     "execution_count": 77,
     "metadata": {},
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    }
   ],
   "source": [
    "top_female_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "93c0b9fd-0ac5-4d51-89e2-82125bac896f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7bf11f1e-dd33-433e-8cac-a748b158dba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_by_diff = mean_ratings.sort_values('diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3bbf046a-7f1d-4921-abc7-97444e158393",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>Dirty Dancing (1987)</th>\n",
       "      <td>3.790378</td>\n",
       "      <td>2.959596</td>\n",
       "      <td>-0.830782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumpin' Jack Flash (1986)</th>\n",
       "      <td>3.254717</td>\n",
       "      <td>2.578358</td>\n",
       "      <td>-0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grease (1978)</th>\n",
       "      <td>3.975265</td>\n",
       "      <td>3.367041</td>\n",
       "      <td>-0.608224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Women (1994)</th>\n",
       "      <td>3.870588</td>\n",
       "      <td>3.321739</td>\n",
       "      <td>-0.548849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel Magnolias (1989)</th>\n",
       "      <td>3.901734</td>\n",
       "      <td>3.365957</td>\n",
       "      <td>-0.535777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "gender                            F         M      diff\n",
       "title                                                  \n",
       "Dirty Dancing (1987)       3.790378  2.959596 -0.830782\n",
       "Jumpin' Jack Flash (1986)  3.254717  2.578358 -0.676359\n",
       "Grease (1978)              3.975265  3.367041 -0.608224\n",
       "Little Women (1994)        3.870588  3.321739 -0.548849\n",
       "Steel Magnolias (1989)     3.901734  3.365957 -0.535777"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff.head()"
   ]
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   "cell_type": "code",
   "execution_count": 81,
   "id": "ce221877-33b3-43d2-a1b3-a8a3698c14d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>Good, The Bad and The Ugly, The (1966)</th>\n",
       "      <td>3.494949</td>\n",
       "      <td>4.221300</td>\n",
       "      <td>0.726351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky Fried Movie, The (1977)</th>\n",
       "      <td>2.878788</td>\n",
       "      <td>3.555147</td>\n",
       "      <td>0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dumb &amp; Dumber (1994)</th>\n",
       "      <td>2.697987</td>\n",
       "      <td>3.336595</td>\n",
       "      <td>0.638608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest Day, The (1962)</th>\n",
       "      <td>3.411765</td>\n",
       "      <td>4.031447</td>\n",
       "      <td>0.619682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cable Guy, The (1996)</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.863787</td>\n",
       "      <td>0.613787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                         F         M      diff\n",
       "title                                                               \n",
       "Good, The Bad and The Ugly, The (1966)  3.494949  4.221300  0.726351\n",
       "Kentucky Fried Movie, The (1977)        2.878788  3.555147  0.676359\n",
       "Dumb & Dumber (1994)                    2.697987  3.336595  0.638608\n",
       "Longest Day, The (1962)                 3.411765  4.031447  0.619682\n",
       "Cable Guy, The (1996)                   2.250000  2.863787  0.613787"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff[::-1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "235fb764-bf5b-4bcf-9cde-c7d036a1dab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_std_by_title = data.groupby('title')['rating'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9731705e-3f35-4994-8f14-2bb02794cd6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_std_by_title = rating_std_by_title.loc[active_titles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "28027d7c-18c7-48ee-8e5d-4b3dd3364b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "'burbs, The (1989)                   1.107760\n",
       "10 Things I Hate About You (1999)    0.989815\n",
       "101 Dalmatians (1961)                0.982103\n",
       "101 Dalmatians (1996)                1.098717\n",
       "12 Angry Men (1957)                  0.812731\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5eb7c49b-f8db-4386-a007-ca00651c0e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Dumb & Dumber (1994)                     1.321333\n",
       "Blair Witch Project, The (1999)          1.316368\n",
       "Natural Born Killers (1994)              1.307198\n",
       "Tank Girl (1995)                         1.277695\n",
       "Rocky Horror Picture Show, The (1975)    1.260177\n",
       "Eyes Wide Shut (1999)                    1.259624\n",
       "Evita (1996)                             1.253631\n",
       "Billy Madison (1995)                     1.249970\n",
       "Fear and Loathing in Las Vegas (1998)    1.246408\n",
       "Bicentennial Man (1999)                  1.245533\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "42cc67d4-9fdc-48ea-8d0d-f68bfb4e7d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Animation|Children's|Comedy\n",
       "1    Adventure|Children's|Fantasy\n",
       "2                  Comedy|Romance\n",
       "3                    Comedy|Drama\n",
       "4                          Comedy\n",
       "Name: genres, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['genres'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e3393d39-f094-4fdb-9bee-9efdf912e0e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [Animation, Children's, Comedy]\n",
       "1    [Adventure, Children's, Fantasy]\n",
       "2                   [Comedy, Romance]\n",
       "3                     [Comedy, Drama]\n",
       "4                            [Comedy]\n",
       "Name: genres, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['genres'].head().str.split('|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ab34df75-fde1-41a3-949e-a376ce09dd86",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies[\"genre\"] = movies.pop(\"genres\").str.split(\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c4473c71-8d45-4ade-b525-50bc434de496",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>[Comedy]</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   movie_id                               title  \\\n",
       "0         1                    Toy Story (1995)   \n",
       "1         2                      Jumanji (1995)   \n",
       "2         3             Grumpier Old Men (1995)   \n",
       "3         4            Waiting to Exhale (1995)   \n",
       "4         5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                              genre  \n",
       "0   [Animation, Children's, Comedy]  \n",
       "1  [Adventure, Children's, Fantasy]  \n",
       "2                 [Comedy, Romance]  \n",
       "3                   [Comedy, Drama]  \n",
       "4                          [Comedy]  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "149ba431-1c4e-4338-a525-a81dea9cce56",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_exploded = movies.explode('genre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "34d77819-d0a9-4a0d-9be1-db2510e39555",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Drama</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   movie_id                     title       genre\n",
       "0         1          Toy Story (1995)   Animation\n",
       "0         1          Toy Story (1995)  Children's\n",
       "0         1          Toy Story (1995)      Comedy\n",
       "1         2            Jumanji (1995)   Adventure\n",
       "1         2            Jumanji (1995)  Children's\n",
       "1         2            Jumanji (1995)     Fantasy\n",
       "2         3   Grumpier Old Men (1995)      Comedy\n",
       "2         3   Grumpier Old Men (1995)     Romance\n",
       "3         4  Waiting to Exhale (1995)      Comedy\n",
       "3         4  Waiting to Exhale (1995)       Drama"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_exploded[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b08bc956-420b-4e62-97ac-bc98ee6f1145",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_with_genre = pd.merge(pd.merge(movies_exploded,ratings),users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "484e916c-7f2a-4a4e-be6e-f74ed7307c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                     1\n",
       "title         Toy Story (1995)\n",
       "genre                Animation\n",
       "user_id                      1\n",
       "rating                       5\n",
       "timestamp            978824268\n",
       "gender                       F\n",
       "age                          1\n",
       "occupation                  10\n",
       "zip                      48067\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_with_genre.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4ba25974-e447-43ed-acf1-6f9668065459",
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_ratings = (ratings_with_genre.groupby([\"genre\", \"age\"])['rating'].mean().unstack('age'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "08f81806-beaf-4bb3-996f-cb9601163249",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
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       "      <td>3.506385</td>\n",
       "      <td>3.447097</td>\n",
       "      <td>3.453358</td>\n",
       "      <td>3.538107</td>\n",
       "      <td>3.528543</td>\n",
       "      <td>3.611333</td>\n",
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       "      <th>Adventure</th>\n",
       "      <td>3.449975</td>\n",
       "      <td>3.408525</td>\n",
       "      <td>3.443163</td>\n",
       "      <td>3.515291</td>\n",
       "      <td>3.528963</td>\n",
       "      <td>3.628163</td>\n",
       "      <td>3.649064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>3.476113</td>\n",
       "      <td>3.624014</td>\n",
       "      <td>3.701228</td>\n",
       "      <td>3.740545</td>\n",
       "      <td>3.734856</td>\n",
       "      <td>3.780020</td>\n",
       "      <td>3.756233</td>\n",
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       "      <th>Children's</th>\n",
       "      <td>3.241642</td>\n",
       "      <td>3.294257</td>\n",
       "      <td>3.426873</td>\n",
       "      <td>3.518423</td>\n",
       "      <td>3.527593</td>\n",
       "      <td>3.556555</td>\n",
       "      <td>3.621822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>3.497491</td>\n",
       "      <td>3.460417</td>\n",
       "      <td>3.490385</td>\n",
       "      <td>3.561984</td>\n",
       "      <td>3.591789</td>\n",
       "      <td>3.646868</td>\n",
       "      <td>3.650949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>3.710170</td>\n",
       "      <td>3.668054</td>\n",
       "      <td>3.680321</td>\n",
       "      <td>3.733736</td>\n",
       "      <td>3.750661</td>\n",
       "      <td>3.810688</td>\n",
       "      <td>3.832549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documentary</th>\n",
       "      <td>3.730769</td>\n",
       "      <td>3.865865</td>\n",
       "      <td>3.946690</td>\n",
       "      <td>3.953747</td>\n",
       "      <td>3.966521</td>\n",
       "      <td>3.908108</td>\n",
       "      <td>3.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>3.794735</td>\n",
       "      <td>3.721930</td>\n",
       "      <td>3.726428</td>\n",
       "      <td>3.782512</td>\n",
       "      <td>3.784356</td>\n",
       "      <td>3.878415</td>\n",
       "      <td>3.933465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>3.317647</td>\n",
       "      <td>3.353778</td>\n",
       "      <td>3.452484</td>\n",
       "      <td>3.482301</td>\n",
       "      <td>3.532468</td>\n",
       "      <td>3.581570</td>\n",
       "      <td>3.532700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>4.145455</td>\n",
       "      <td>3.997368</td>\n",
       "      <td>4.058725</td>\n",
       "      <td>4.064910</td>\n",
       "      <td>4.105376</td>\n",
       "      <td>4.175401</td>\n",
       "      <td>4.125932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age                1         18        25        35        45        50  \\\n",
       "genre                                                                     \n",
       "Action       3.506385  3.447097  3.453358  3.538107  3.528543  3.611333   \n",
       "Adventure    3.449975  3.408525  3.443163  3.515291  3.528963  3.628163   \n",
       "Animation    3.476113  3.624014  3.701228  3.740545  3.734856  3.780020   \n",
       "Children's   3.241642  3.294257  3.426873  3.518423  3.527593  3.556555   \n",
       "Comedy       3.497491  3.460417  3.490385  3.561984  3.591789  3.646868   \n",
       "Crime        3.710170  3.668054  3.680321  3.733736  3.750661  3.810688   \n",
       "Documentary  3.730769  3.865865  3.946690  3.953747  3.966521  3.908108   \n",
       "Drama        3.794735  3.721930  3.726428  3.782512  3.784356  3.878415   \n",
       "Fantasy      3.317647  3.353778  3.452484  3.482301  3.532468  3.581570   \n",
       "Film-Noir    4.145455  3.997368  4.058725  4.064910  4.105376  4.175401   \n",
       "\n",
       "age                56  \n",
       "genre                  \n",
       "Action       3.610709  \n",
       "Adventure    3.649064  \n",
       "Animation    3.756233  \n",
       "Children's   3.621822  \n",
       "Comedy       3.650949  \n",
       "Crime        3.832549  \n",
       "Documentary  3.961538  \n",
       "Drama        3.933465  \n",
       "Fantasy      3.532700  \n",
       "Film-Noir    4.125932  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_ratings[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0cf27655-53f2-4fb7-b74d-dac70181a295",
   "metadata": {},
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv('datasets/babynames/yob1880.txt',names=[\"name\", \"sex\", \"births\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bf1cbeac-d6ab-4137-a13a-2d0b2f5df491",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births\n",
       "0          Mary   F    7065\n",
       "1          Anna   F    2604\n",
       "2          Emma   F    2003\n",
       "3     Elizabeth   F    1939\n",
       "4        Minnie   F    1746\n",
       "...         ...  ..     ...\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6e373f23-c80c-4773-b484-a04f613cd551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex')['births'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "911c3de6-b1eb-4fdd-95c0-fd5495123931",
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces=[]\n",
    "for year in range(1880,2011):\n",
    "    path = f'datasets/babynames/yob{year}.txt'\n",
    "    frame = pd.read_csv(path,names= [\"name\", \"sex\", \"births\"])\n",
    "    frame['year']=year\n",
    "    pieces.append(frame)\n",
    "\n",
    "names = pd.concat(pieces,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a476599f-e5e6-4dd0-a40d-e46b6f4356b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year\n",
       "0             Mary   F    7065  1880\n",
       "1             Anna   F    2604  1880\n",
       "2             Emma   F    2003  1880\n",
       "3        Elizabeth   F    1939  1880\n",
       "4           Minnie   F    1746  1880\n",
       "...            ...  ..     ...   ...\n",
       "1690779    Zymaire   M       5  2010\n",
       "1690780     Zyonne   M       5  2010\n",
       "1690781  Zyquarius   M       5  2010\n",
       "1690782      Zyran   M       5  2010\n",
       "1690783      Zzyzx   M       5  2010\n",
       "\n",
       "[1690784 rows x 4 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d6257458-ff1f-4b84-bb48-ae1b359af1fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_42140\\2397486732.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  total_births = names.pivot_table('births',index='year',columns='sex',aggfunc=sum)\n"
     ]
    }
   ],
   "source": [
    "total_births = names.pivot_table('births',index='year',columns='sex',aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3b8cf1df-392b-4317-92ed-48c1633d736e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2bc7d88a-4c31-4325-81a7-dc1e9e51f085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Total births by sex and year'}, xlabel='year'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title=\"Total births by sex and year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9f88f889-3e95-4f20-85e9-1aca66f7c927",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    group['prop'] = group['births'] / group['births'].sum()\n",
    "    return group\n",
    "names = names.groupby([\"year\", \"sex\"],group_keys=False).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "30d6418e-329a-4797-8ef3-6d962868198f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year      prop\n",
       "0             Mary   F    7065  1880  0.077643\n",
       "1             Anna   F    2604  1880  0.028618\n",
       "2             Emma   F    2003  1880  0.022013\n",
       "3        Elizabeth   F    1939  1880  0.021309\n",
       "4           Minnie   F    1746  1880  0.019188\n",
       "...            ...  ..     ...   ...       ...\n",
       "1690779    Zymaire   M       5  2010  0.000003\n",
       "1690780     Zyonne   M       5  2010  0.000003\n",
       "1690781  Zyquarius   M       5  2010  0.000003\n",
       "1690782      Zyran   M       5  2010  0.000003\n",
       "1690783      Zzyzx   M       5  2010  0.000003\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e7b3d14b-93f6-4fe6-bb8f-2a22544ac7c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "            ... \n",
       "2008  M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby([\"year\", \"sex\"])['prop'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "802a89c9-1bb8-4278-a5bc-40a229580938",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values('births',ascending=False)[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f2de6cc7-78cd-4713-ad13-cbbcc5999780",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = names.groupby(['year','sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3316f951-bf49-4654-96d2-0e1a606a76da",
   "metadata": {},
   "outputs": [],
   "source": [
    "top1000 = grouped.apply(get_top1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5aea9447-3a74-4ef5-a607-81551dbcdee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name sex  births  year      prop\n",
       "year sex                                         \n",
       "1880 F   0       Mary   F    7065  1880  0.077643\n",
       "         1       Anna   F    2604  1880  0.028618\n",
       "         2       Emma   F    2003  1880  0.022013\n",
       "         3  Elizabeth   F    1939  1880  0.021309\n",
       "         4     Minnie   F    1746  1880  0.019188"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d8b919e3-b979-441e-b665-d873c5b3e80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "top1000 = top1000.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "71953aaa-0612-4e0e-82c8-58afd2ce2bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d95e3097-b212-42aa-925e-1d38aaa7dd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "boys = top1000[top1000['sex'] == 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ab138488-10a3-4df9-8c7c-21df53a4a18f",
   "metadata": {},
   "outputs": [],
   "source": [
    "girls = top1000[top1000['sex'] == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "010c41f2-7f72-477f-bcff-d838edc53f20",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_42140\\478795628.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  total_births = top1000.pivot_table('births',index='year',columns='name',aggfunc=sum)\n"
     ]
    }
   ],
   "source": [
    "total_births = top1000.pivot_table('births',index='year',columns='name',aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "dc9d7752-ac82-4fc9-85c1-16cf6b5b7672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "3f807cad-c1ca-433c-8657-38e9ddb1ce95",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = total_births[[\"John\", \"Harry\", \"Mary\", \"Marilyn\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "574188c7-71d3-4d88-b93b-16877d442041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: xlabel='year'>, <Axes: xlabel='year'>,\n",
       "       <Axes: xlabel='year'>, <Axes: xlabel='year'>], dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset.plot(subplots=True,figsize=(12,10),title='Number of births per year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "4c8ccaa9-3659-426c-bc75-e2e8893c3a28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_42140\\991953680.py:1: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  table = top1000.pivot_table('prop',index='year',columns='sex',aggfunc=sum)\n"
     ]
    }
   ],
   "source": [
    "table = top1000.pivot_table('prop',index='year',columns='sex',aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "7dd23f31-4eef-4fd0-b11e-f445bace0c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Sum of table1000.prop by year and sex'}, xlabel='year'>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(title='Sum of table1000.prop by year and sex',yticks=np.linspace(0,1.2,13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "26b2a8a3-9e18-47cc-ab38-c9e92239c5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = boys[boys['year']==2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "851dc34d-fe1c-475d-b3b7-5f060b22f051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births  year      prop\n",
       "260877    Jacob   M   21875  2010  0.011523\n",
       "260878    Ethan   M   17866  2010  0.009411\n",
       "260879  Michael   M   17133  2010  0.009025\n",
       "260880   Jayden   M   17030  2010  0.008971\n",
       "260881  William   M   16870  2010  0.008887\n",
       "...         ...  ..     ...   ...       ...\n",
       "261872   Camilo   M     194  2010  0.000102\n",
       "261873   Destin   M     194  2010  0.000102\n",
       "261874   Jaquan   M     194  2010  0.000102\n",
       "261875   Jaydan   M     194  2010  0.000102\n",
       "261876   Maxton   M     193  2010  0.000102\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "d4fa5b4e-2ecf-4771-98ae-f9a08279ee34",
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_cumsum = df['prop'].sort_values(ascending=False).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "acef56a4-82f3-4eac-844d-42032a46cb66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "22152842-193e-4f74-aadd-a1785d135e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4b17e4db-2cd7-4b43-bdd2-379706c966ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = boys[boys.year==1900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "2b2a7130-9c7c-4928-8d5c-6123a2a13812",
   "metadata": {},
   "outputs": [],
   "source": [
    "in1900 = df.sort_values('prop',ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d318be8d-75ab-4a09-a22e-abdde9a1d261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in1900.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "f0f39ee7-dbd0-4428-9449-666e527b471a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(\"prop\", ascending=False)\n",
    "    return group.prop.cumsum().searchsorted(q) + 1\n",
    "\n",
    "diversity = top1000.groupby([\"year\", \"sex\"]).apply(get_quantile_count)\n",
    "diversity = diversity.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "968a91c1-8e10-486f-a756-fe117ef3cb93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fb9e9b34-09d8-4b3c-8fe9-37cff1b7a312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Number of popular names in top 50%'}, xlabel='year'>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "e48babef-4d9b-4266-a85d-a9212f98d7c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_42140\\121524359.py:7: FutureWarning: The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  table = names.pivot_table(\"births\", index=last_letters,\n"
     ]
    }
   ],
   "source": [
    "def get_last_letter(x):\n",
    "    return x[-1]\n",
    "\n",
    "last_letters = names[\"name\"].map(get_last_letter)\n",
    "last_letters.name = \"last_letter\"\n",
    "\n",
    "table = names.pivot_table(\"births\", index=last_letters,\n",
    "                          columns=[\"sex\", \"year\"], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ebe09fc9-9656-4efe-ab1b-56e9badfd0db",
   "metadata": {},
   "outputs": [],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level=\"year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "91e5b3ba-d796-4725-9b46-2114e9002c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "91e00c55-eb97-48a8-b79c-30edbec6e652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c523eee2-4cb6-433d-bc23-aa5af59e33ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "letter_prop = subtable / subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "e4d37ebe-6063-43e1-afc3-21c024abf7ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.001404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.075852</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>0.037907</td>\n",
       "      <td>0.051670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.039965</td>\n",
       "      <td>0.031734</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.022628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.049384</td>\n",
       "      <td>0.018541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>0.043189</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>0.026356</td>\n",
       "      <td>0.065016</td>\n",
       "      <td>0.104904</td>\n",
       "      <td>0.070367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.008613</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.058044</td>\n",
       "      <td>0.033827</td>\n",
       "      <td>0.024657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.079240</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>0.140210</td>\n",
       "      <td>0.143415</td>\n",
       "      <td>0.152522</td>\n",
       "      <td>0.362771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.012829</td>\n",
       "      <td>0.042681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.005675</td>\n",
       "      <td>0.001269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.031034</td>\n",
       "      <td>0.087477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.039042</td>\n",
       "      <td>0.012764</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.130815</td>\n",
       "      <td>0.102730</td>\n",
       "      <td>0.065145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.027438</td>\n",
       "      <td>0.015201</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>0.072879</td>\n",
       "      <td>0.065655</td>\n",
       "      <td>0.022861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
       "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
       "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
       "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
       "j                 NaN       NaN  0.000090       NaN       NaN  0.000769\n",
       "k            0.000121  0.000156  0.000356  0.036581  0.049384  0.018541\n",
       "l            0.043189  0.033867  0.026356  0.065016  0.104904  0.070367\n",
       "m            0.001201  0.008613  0.002588  0.058044  0.033827  0.024657\n",
       "n            0.079240  0.130687  0.140210  0.143415  0.152522  0.362771\n",
       "o            0.001660  0.002439  0.001243  0.017065  0.012829  0.042681\n",
       "p            0.000018  0.000023  0.000020  0.003172  0.005675  0.001269\n",
       "q                 NaN       NaN  0.000030       NaN       NaN  0.000180\n",
       "r            0.013390  0.006764  0.018025  0.064481  0.031034  0.087477\n",
       "s            0.039042  0.012764  0.013332  0.130815  0.102730  0.065145\n",
       "t            0.027438  0.015201  0.007830  0.072879  0.065655  0.022861\n",
       "u            0.000684  0.000574  0.000417  0.000124  0.000057  0.001221\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "232aa60e-6536-47c6-8d62-8ca373805446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Female'}, xlabel='last_letter'>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop[\"M\"].plot(kind=\"bar\", rot=0, ax=axes[0], title=\"Male\")\n",
    "letter_prop[\"F\"].plot(kind=\"bar\", rot=0, ax=axes[1], title=\"Female\",\n",
    "                      legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "16a1ac9e-34a0-4af0-9430-28edab30ce62",
   "metadata": {},
   "outputs": [],
   "source": [
    "letter_prop = table / table.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "08359db5-c3e8-48c8-bdd8-cfcfce5312ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "dny_ts = letter_prop.loc[[\"d\", \"n\", \"y\"], \"M\"].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "900c2480-7222-478c-a5e1-66d75c0f8365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "07f5d8a6-1ac1-49be-a060-6aded152b562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year'>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "da358252-5308-4d1c-9a9d-0fe0a20286d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_names = pd.Series(top1000[\"name\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "a7706132-c614-49c3-ae5f-887e04a3b12f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lesley_like = all_names[all_names.str.contains(\"Lesl\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "19b1c54c-f64b-4eac-a563-97e69c8ed7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9c066a18-6c7d-4761-9ac1-e8228293f8f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered = top1000[top1000[\"name\"].isin(lesley_like)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "959d5988-ce6a-4ffd-8564-50716ee155e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered.groupby(\"name\")[\"births\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "05d12793-f731-4023-90a8-322421ebf021",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = filtered.pivot_table(\"births\", index=\"year\",columns=\"sex\", aggfunc=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "cc21c321-6a7a-4a63-8322-4fa95fc77643",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = table.div(table.sum(axis=\"columns\"), axis=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ea521ab8-ad53-43b6-a742-0143301204d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     F   M\n",
       "year         \n",
       "2006  1.0 NaN\n",
       "2007  1.0 NaN\n",
       "2008  1.0 NaN\n",
       "2009  1.0 NaN\n",
       "2010  1.0 NaN"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "512bf03a-5cd4-40c5-a782-6b45ffc25c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year'>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={\"M\": \"k-\", \"F\": \"k--\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b5847f17-cf7a-43ae-9449-e72dd6d2538d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "ea9c83bd-9a02-48e7-b960-0fc36c626020",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = json.load(open('datasets/usda_food/database.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "47be9b61-3ece-48d6-a5bc-0fb28f62d41f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f6c5d4f4-6a8f-49b8-ab7b-2e8443a229a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id', 'description', 'tags', 'manufacturer', 'group', 'portions', 'nutrients'])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d52765a1-4107-4fa5-a918-ce6c612c6718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'value': 25.18,\n",
       " 'units': 'g',\n",
       " 'description': 'Protein',\n",
       " 'group': 'Composition'}"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0]['nutrients'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "3513d6e1-37f6-47eb-bcd4-8a09c4e4a543",
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients  = pd.DataFrame(db[0]['nutrients'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ef23d548-6010-40d4-afe8-e83cfd9b674c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.18</td>\n",
       "      <td>g</td>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.20</td>\n",
       "      <td>g</td>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.06</td>\n",
       "      <td>g</td>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.28</td>\n",
       "      <td>g</td>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>376.00</td>\n",
       "      <td>kcal</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.28</td>\n",
       "      <td>g</td>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1573.00</td>\n",
       "      <td>kJ</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     value units                  description        group\n",
       "0    25.18     g                      Protein  Composition\n",
       "1    29.20     g            Total lipid (fat)  Composition\n",
       "2     3.06     g  Carbohydrate, by difference  Composition\n",
       "3     3.28     g                          Ash        Other\n",
       "4   376.00  kcal                       Energy       Energy\n",
       "5    39.28     g                        Water  Composition\n",
       "6  1573.00    kJ                       Energy       Energy"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "06c93d43-85f9-430b-a320-60ead08cca6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "info_keys = [\"description\", \"group\", \"id\", \"manufacturer\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "d8e33662-32f8-45b4-9e18-83f51986dc48",
   "metadata": {},
   "outputs": [],
   "source": [
    "info = pd.DataFrame(db,columns=info_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "83b54c0d-fcb0-4896-b67b-949b6da8d3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td></td>\n",
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       "      <th>3</th>\n",
       "      <td>Cheese, feta</td>\n",
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       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cheese, mozzarella, part skim milk</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
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       "                          description                   group    id  \\\n",
       "0                     Cheese, caraway  Dairy and Egg Products  1008   \n",
       "1                     Cheese, cheddar  Dairy and Egg Products  1009   \n",
       "2                        Cheese, edam  Dairy and Egg Products  1018   \n",
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       "4  Cheese, mozzarella, part skim milk  Dairy and Egg Products  1028   \n",
       "\n",
       "  manufacturer  \n",
       "0               \n",
       "1               \n",
       "2               \n",
       "3               \n",
       "4               "
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6636 entries, 0 to 6635\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
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      " 1   group         6636 non-null   object\n",
      " 2   id            6636 non-null   int64 \n",
      " 3   manufacturer  5195 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 207.5+ KB\n"
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   "cell_type": "code",
   "execution_count": 169,
   "id": "1ca24042-0952-45da-aa02-af655ca58d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_42140\\3406835181.py:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  pd.value_counts(info['group'])[:10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "group\n",
       "Vegetables and Vegetable Products    812\n",
       "Beef Products                        618\n",
       "Baked Products                       496\n",
       "Breakfast Cereals                    403\n",
       "Legumes and Legume Products          365\n",
       "Fast Foods                           365\n",
       "Lamb, Veal, and Game Products        345\n",
       "Sweets                               341\n",
       "Fruits and Fruit Juices              328\n",
       "Pork Products                        328\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "pd.value_counts(info['group'])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "17614362-a8ef-4005-9411-630dfd600c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients=[]\n",
    "\n",
    "for rec in db:\n",
    "    fnuts = pd.DataFrame(rec['nutrients'])\n",
    "    fnuts['id'] = rec['id']\n",
    "    nutrients.append(fnuts)\n",
    "\n",
    "nutrients = pd.concat(nutrients,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "d6ae92c5-8a75-4a4e-b475-24cd9cdc2c19",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000</td>\n",
       "      <td>mg</td>\n",
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       "      <td>43546</td>\n",
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       "    <tr>\n",
       "      <th>389352</th>\n",
       "      <td>0.072</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389353</th>\n",
       "      <td>0.028</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
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       "    <tr>\n",
       "      <th>389354</th>\n",
       "      <td>0.041</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>389355 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          value units                         description        group     id\n",
       "0        25.180     g                             Protein  Composition   1008\n",
       "1        29.200     g                   Total lipid (fat)  Composition   1008\n",
       "2         3.060     g         Carbohydrate, by difference  Composition   1008\n",
       "3         3.280     g                                 Ash        Other   1008\n",
       "4       376.000  kcal                              Energy       Energy   1008\n",
       "...         ...   ...                                 ...          ...    ...\n",
       "389350    0.000   mcg                 Vitamin B-12, added     Vitamins  43546\n",
       "389351    0.000    mg                         Cholesterol        Other  43546\n",
       "389352    0.072     g        Fatty acids, total saturated        Other  43546\n",
       "389353    0.028     g  Fatty acids, total monounsaturated        Other  43546\n",
       "389354    0.041     g  Fatty acids, total polyunsaturated        Other  43546\n",
       "\n",
       "[389355 rows x 5 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "nutrients"
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  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "da527ef5-5cea-4d71-89d8-2950341197b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14179"
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     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "nutrients.duplicated().sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "027222c5-5e89-4764-967a-628ada7385de",
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients = nutrients.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "576d770f-8fa7-4d37-9fca-cc5d416f9feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_mapping={\"description\" : \"food\",\"group\": \"fgroup\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "21b9a243-87f9-4a37-ae40-f5da6a149d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "info = info.rename(columns=col_mapping,copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "4e022e73-f727-4e8b-8519-0dc72f533486",
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    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6636 entries, 0 to 6635\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   food          6636 non-null   object\n",
      " 1   fgroup        6636 non-null   object\n",
      " 2   id            6636 non-null   int64 \n",
      " 3   manufacturer  5195 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 207.5+ KB\n"
     ]
    }
   ],
   "source": [
    "info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "f9c5e0b6-4ab6-49b3-80be-dae1e23d5712",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_mapping={\"description\" : \"nutrient\",\"group\" : \"nutgroup\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "3bdc1a82-bf4c-4bd0-9d73-af1a1f3e43cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients = nutrients.rename(columns=col_mapping,copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "f04563b8-d386-479f-8fca-3d5d46c79a5d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389354</th>\n",
       "      <td>0.041</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375176 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          value units                            nutrient     nutgroup     id\n",
       "0        25.180     g                             Protein  Composition   1008\n",
       "1        29.200     g                   Total lipid (fat)  Composition   1008\n",
       "2         3.060     g         Carbohydrate, by difference  Composition   1008\n",
       "3         3.280     g                                 Ash        Other   1008\n",
       "4       376.000  kcal                              Energy       Energy   1008\n",
       "...         ...   ...                                 ...          ...    ...\n",
       "389350    0.000   mcg                 Vitamin B-12, added     Vitamins  43546\n",
       "389351    0.000    mg                         Cholesterol        Other  43546\n",
       "389352    0.072     g        Fatty acids, total saturated        Other  43546\n",
       "389353    0.028     g  Fatty acids, total monounsaturated        Other  43546\n",
       "389354    0.041     g  Fatty acids, total polyunsaturated        Other  43546\n",
       "\n",
       "[375176 rows x 5 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "5784b077-0b34-409b-bfed-6dbec5c11b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndata = pd.merge(nutrients,info,on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "9bb4f45c-5dfa-4e5e-9732-5ae9fd1a2398",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 375176 entries, 0 to 375175\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   value         375176 non-null  float64\n",
      " 1   units         375176 non-null  object \n",
      " 2   nutrient      375176 non-null  object \n",
      " 3   nutgroup      375176 non-null  object \n",
      " 4   id            375176 non-null  int64  \n",
      " 5   food          375176 non-null  object \n",
      " 6   fgroup        375176 non-null  object \n",
      " 7   manufacturer  293054 non-null  object \n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 22.9+ MB\n"
     ]
    }
   ],
   "source": [
    "ndata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "59b0c522-ac28-45f1-8840-1de50bec89eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value                                             0.04\n",
       "units                                                g\n",
       "nutrient                                       Glycine\n",
       "nutgroup                                   Amino Acids\n",
       "id                                                6158\n",
       "food            Soup, tomato bisque, canned, condensed\n",
       "fgroup                      Soups, Sauces, and Gravies\n",
       "manufacturer                                          \n",
       "Name: 30000, dtype: object"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata.iloc[30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "3fa26e2c-ed2f-41a3-bf6f-9866e0f9c179",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ndata.groupby(['nutrient','fgroup'])['value'].quantile(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "843a34c4-c933-4ebf-b79a-8a94ef46d699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='fgroup'>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result[\"Zinc, Zn\"].sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "efb77159-bbf0-46e1-8008-cc35a3cafba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "by_nutrient = ndata.groupby([\"nutgroup\", \"nutrient\"])\n",
    "\n",
    "def get_maximum(x):\n",
    "    return x.loc[x.value.idxmax()]\n",
    "\n",
    "max_foods = by_nutrient.apply(get_maximum)[[\"value\", \"food\"]]\n",
    "\n",
    "max_foods[\"food\"] = max_foods[\"food\"].str[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a7e02fb9-b8ad-49bc-86e5-3f2e7b426984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nutrient\n",
       "Alanine                           Gelatins, dry powder, unsweetened\n",
       "Arginine                               Seeds, sesame flour, low-fat\n",
       "Aspartic acid                                   Soy protein isolate\n",
       "Cystine                Seeds, cottonseed flour, low fat (glandless)\n",
       "Glutamic acid                                   Soy protein isolate\n",
       "Glycine                           Gelatins, dry powder, unsweetened\n",
       "Histidine                Whale, beluga, meat, dried (Alaska Native)\n",
       "Hydroxyproline    KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA...\n",
       "Isoleucine        Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Leucine           Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Lysine            Seal, bearded (Oogruk), meat, dried (Alaska Na...\n",
       "Methionine                    Fish, cod, Atlantic, dried and salted\n",
       "Phenylalanine     Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Proline                           Gelatins, dry powder, unsweetened\n",
       "Serine            Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Threonine         Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Tryptophan         Sea lion, Steller, meat with fat (Alaska Native)\n",
       "Tyrosine          Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Valine            Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Name: food, dtype: object"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_foods.loc[\"Amino Acids\"][\"food\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "716bba7d-0ef4-4eb8-8dc9-a0f4f013d613",
   "metadata": {},
   "outputs": [],
   "source": [
    "fec = pd.read_csv('datasets/fec/P00000001-ALL.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "3bceb4f3-efc8-457d-86aa-ea63e90e5195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count    Dtype  \n",
      "---  ------             --------------    -----  \n",
      " 0   cmte_id            1001731 non-null  object \n",
      " 1   cand_id            1001731 non-null  object \n",
      " 2   cand_nm            1001731 non-null  object \n",
      " 3   contbr_nm          1001731 non-null  object \n",
      " 4   contbr_city        1001712 non-null  object \n",
      " 5   contbr_st          1001727 non-null  object \n",
      " 6   contbr_zip         1001620 non-null  object \n",
      " 7   contbr_employer    988002 non-null   object \n",
      " 8   contbr_occupation  993301 non-null   object \n",
      " 9   contb_receipt_amt  1001731 non-null  float64\n",
      " 10  contb_receipt_dt   1001731 non-null  object \n",
      " 11  receipt_desc       14166 non-null    object \n",
      " 12  memo_cd            92482 non-null    object \n",
      " 13  memo_text          97770 non-null    object \n",
      " 14  form_tp            1001731 non-null  object \n",
      " 15  file_num           1001731 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "fec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "a752a1bd-4982-4447-8b25-740562bcfe69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmte_id                             C00431445\n",
       "cand_id                             P80003338\n",
       "cand_nm                         Obama, Barack\n",
       "contbr_nm                         ELLMAN, IRA\n",
       "contbr_city                             TEMPE\n",
       "contbr_st                                  AZ\n",
       "contbr_zip                          852816719\n",
       "contbr_employer      ARIZONA STATE UNIVERSITY\n",
       "contbr_occupation                   PROFESSOR\n",
       "contb_receipt_amt                        50.0\n",
       "contb_receipt_dt                    01-DEC-11\n",
       "receipt_desc                              NaN\n",
       "memo_cd                                   NaN\n",
       "memo_text                                 NaN\n",
       "form_tp                                 SA17A\n",
       "file_num                               772372\n",
       "Name: 123456, dtype: object"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.iloc[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "0490adf9-5e4b-485c-ac82-7647b7ed8dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_cands = fec[\"cand_nm\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "51d4a734-18da-4ba6-8f9a-e99d70fe64bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
       "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
       "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "404b99b9-3a74-4654-824c-e9b5e384a01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Obama, Barack'"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "4cfd879c-82e8-421c-b06c-aecfe5b5c509",
   "metadata": {},
   "outputs": [],
   "source": [
    "parties = {\"Bachmann, Michelle\": \"Republican\",\n",
    "           \"Cain, Herman\": \"Republican\",\n",
    "           \"Gingrich, Newt\": \"Republican\",\n",
    "           \"Huntsman, Jon\": \"Republican\",\n",
    "           \"Johnson, Gary Earl\": \"Republican\",\n",
    "           \"McCotter, Thaddeus G\": \"Republican\",\n",
    "           \"Obama, Barack\": \"Democrat\",\n",
    "           \"Paul, Ron\": \"Republican\",\n",
    "           \"Pawlenty, Timothy\": \"Republican\",\n",
    "           \"Perry, Rick\": \"Republican\",\n",
    "           \"Roemer, Charles E. 'Buddy' III\": \"Republican\",\n",
    "           \"Romney, Mitt\": \"Republican\",\n",
    "           \"Santorum, Rick\": \"Republican\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "4fff4675-8417-4ac4-b4ef-5ffb89efdca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Obama, Barack\n",
       "123457    Obama, Barack\n",
       "123458    Obama, Barack\n",
       "123459    Obama, Barack\n",
       "123460    Obama, Barack\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"cand_nm\"][123456:123461]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "97cfaae1-c2d4-4cc3-938b-f6f784238380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Democrat\n",
       "123457    Democrat\n",
       "123458    Democrat\n",
       "123459    Democrat\n",
       "123460    Democrat\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"cand_nm\"][123456:123461].map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "f2443a36-b595-4cef-b771-0fedb6e00481",
   "metadata": {},
   "outputs": [],
   "source": [
    "fec[\"party\"] = fec[\"cand_nm\"].map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "83c09117-33e4-41cd-bba1-f2e62293590c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "party\n",
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"party\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "340d7866-dd08-4f0d-a582-fae222942a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contb_receipt_amt\n",
       "True     991475\n",
       "False     10256\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fec[\"contb_receipt_amt\"] > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "cf3af5f7-dbd4-4d1c-a09c-b7a11915f937",
   "metadata": {},
   "outputs": [],
   "source": [
    "fec = fec[fec[\"contb_receipt_amt\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "7d18a4a3-ea64-4ada-9727-6e90aef2d1f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fec_mrbo = fec[fec[\"cand_nm\"].isin([\"Obama, Barack\", \"Romney, Mitt\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "6706517a-9624-45e2-ac05-dd6da55f313e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contbr_occupation\n",
       "RETIRED                                   233990\n",
       "INFORMATION REQUESTED                      35107\n",
       "ATTORNEY                                   34286\n",
       "HOMEMAKER                                  29931\n",
       "PHYSICIAN                                  23432\n",
       "INFORMATION REQUESTED PER BEST EFFORTS     21138\n",
       "ENGINEER                                   14334\n",
       "TEACHER                                    13990\n",
       "CONSULTANT                                 13273\n",
       "PROFESSOR                                  12555\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"contbr_occupation\"].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "467b044c-5fe3-44b6-aac8-f2d159550ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "occ_mapping = {\n",
    "   \"INFORMATION REQUESTED PER BEST EFFORTS\" : \"NOT PROVIDED\",\n",
    "   \"INFORMATION REQUESTED\" : \"NOT PROVIDED\",\n",
    "   \"INFORMATION REQUESTED (BEST EFFORTS)\" : \"NOT PROVIDED\",\n",
    "   \"C.E.O.\": \"CEO\"\n",
    "}\n",
    "\n",
    "def get_occ(x):\n",
    "    # If no mapping provided, return x\n",
    "    return occ_mapping.get(x, x)\n",
    "\n",
    "fec[\"contbr_occupation\"] = fec[\"contbr_occupation\"].map(get_occ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "095f1dab-8f09-425a-b14b-c696de6d94ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_mapping = {\n",
    "   \"INFORMATION REQUESTED PER BEST EFFORTS\" : \"NOT PROVIDED\",\n",
    "   \"INFORMATION REQUESTED\" : \"NOT PROVIDED\",\n",
    "   \"SELF\" : \"SELF-EMPLOYED\",\n",
    "   \"SELF EMPLOYED\" : \"SELF-EMPLOYED\",\n",
    "}\n",
    "\n",
    "def get_emp(x):\n",
    "    # If no mapping provided, return x\n",
    "    return emp_mapping.get(x, x)\n",
    "\n",
    "fec[\"contbr_employer\"] = fec[\"contbr_employer\"].map(get_emp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "66c6a21e-9848-4ca1-8035-a6d4d7e15e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "by_occupation = fec.pivot_table(\"contb_receipt_amt\",\n",
    "                                index=\"contbr_occupation\",\n",
    "                                columns=\"party\", aggfunc=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "2f99063b-f944-42fd-940b-ba789acba878",
   "metadata": {},
   "outputs": [],
   "source": [
    "over_2mm = by_occupation[by_occupation.sum(axis=\"columns\") > 2000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "c9c1f7ce-3523-4237-8c2b-6e7590de8cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATTORNEY</th>\n",
       "      <td>11141982.97</td>\n",
       "      <td>7477194.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEO</th>\n",
       "      <td>2074974.79</td>\n",
       "      <td>4211040.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSULTANT</th>\n",
       "      <td>2459912.71</td>\n",
       "      <td>2544725.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGINEER</th>\n",
       "      <td>951525.55</td>\n",
       "      <td>1818373.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXECUTIVE</th>\n",
       "      <td>1355161.05</td>\n",
       "      <td>4138850.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <td>4248875.80</td>\n",
       "      <td>13634275.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVESTOR</th>\n",
       "      <td>884133.00</td>\n",
       "      <td>2431768.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAWYER</th>\n",
       "      <td>3160478.87</td>\n",
       "      <td>391224.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGER</th>\n",
       "      <td>762883.22</td>\n",
       "      <td>1444532.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOT PROVIDED</th>\n",
       "      <td>4866973.96</td>\n",
       "      <td>20565473.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWNER</th>\n",
       "      <td>1001567.36</td>\n",
       "      <td>2408286.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHYSICIAN</th>\n",
       "      <td>3735124.94</td>\n",
       "      <td>3594320.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESIDENT</th>\n",
       "      <td>1878509.95</td>\n",
       "      <td>4720923.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROFESSOR</th>\n",
       "      <td>2165071.08</td>\n",
       "      <td>296702.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REAL ESTATE</th>\n",
       "      <td>528902.09</td>\n",
       "      <td>1625902.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIRED</th>\n",
       "      <td>25305116.38</td>\n",
       "      <td>23561244.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <td>672393.40</td>\n",
       "      <td>1640252.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "party                 Democrat   Republican\n",
       "contbr_occupation                          \n",
       "ATTORNEY           11141982.97   7477194.43\n",
       "CEO                 2074974.79   4211040.52\n",
       "CONSULTANT          2459912.71   2544725.45\n",
       "ENGINEER             951525.55   1818373.70\n",
       "EXECUTIVE           1355161.05   4138850.09\n",
       "HOMEMAKER           4248875.80  13634275.78\n",
       "INVESTOR             884133.00   2431768.92\n",
       "LAWYER              3160478.87    391224.32\n",
       "MANAGER              762883.22   1444532.37\n",
       "NOT PROVIDED        4866973.96  20565473.01\n",
       "OWNER               1001567.36   2408286.92\n",
       "PHYSICIAN           3735124.94   3594320.24\n",
       "PRESIDENT           1878509.95   4720923.76\n",
       "PROFESSOR           2165071.08    296702.73\n",
       "REAL ESTATE          528902.09   1625902.25\n",
       "RETIRED            25305116.38  23561244.49\n",
       "SELF-EMPLOYED        672393.40   1640252.54"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_2mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "bf35930c-0705-490c-8282-e7952a421628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='contbr_occupation'>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "over_2mm.plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "f5e02183-aaee-41f6-845c-639fc8e7f1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_amounts(group, key, n=5):\n",
    "    totals = group.groupby(key)[\"contb_receipt_amt\"].sum()\n",
    "    return totals.nlargest(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "1d0a6eaa-ee65-4a3f-8c0f-0d4d8097de84",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = fec_mrbo.groupby(\"cand_nm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "9f54a2a4-366e-4578-b656-85a716241322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_occupation                     \n",
       "Obama, Barack  RETIRED                                   25305116.38\n",
       "               ATTORNEY                                  11141982.97\n",
       "               INFORMATION REQUESTED                      4866973.96\n",
       "               HOMEMAKER                                  4248875.80\n",
       "               PHYSICIAN                                  3735124.94\n",
       "               LAWYER                                     3160478.87\n",
       "               CONSULTANT                                 2459912.71\n",
       "Romney, Mitt   RETIRED                                   11508473.59\n",
       "               INFORMATION REQUESTED PER BEST EFFORTS    11396894.84\n",
       "               HOMEMAKER                                  8147446.22\n",
       "               ATTORNEY                                   5364718.82\n",
       "               PRESIDENT                                  2491244.89\n",
       "               EXECUTIVE                                  2300947.03\n",
       "               C.E.O.                                     1968386.11\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_top_amounts, \"contbr_occupation\", n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "cff8c0ad-fd78-4a0c-98f5-5209a222278d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_employer                       \n",
       "Obama, Barack  RETIRED                                   22694358.85\n",
       "               SELF-EMPLOYED                             17080985.96\n",
       "               NOT EMPLOYED                               8586308.70\n",
       "               INFORMATION REQUESTED                      5053480.37\n",
       "               HOMEMAKER                                  2605408.54\n",
       "               SELF                                       1076531.20\n",
       "               SELF EMPLOYED                               469290.00\n",
       "               STUDENT                                     318831.45\n",
       "               VOLUNTEER                                   257104.00\n",
       "               MICROSOFT                                   215585.36\n",
       "Romney, Mitt   INFORMATION REQUESTED PER BEST EFFORTS    12059527.24\n",
       "               RETIRED                                   11506225.71\n",
       "               HOMEMAKER                                  8147196.22\n",
       "               SELF-EMPLOYED                              7409860.98\n",
       "               STUDENT                                     496490.94\n",
       "               CREDIT SUISSE                               281150.00\n",
       "               MORGAN STANLEY                              267266.00\n",
       "               GOLDMAN SACH & CO.                          238250.00\n",
       "               BARCLAYS CAPITAL                            162750.00\n",
       "               H.I.G. CAPITAL                              139500.00\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_top_amounts, \"contbr_employer\", n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "4164b4f5-0cf4-4273-afd8-1741af85a360",
   "metadata": {},
   "outputs": [],
   "source": [
    " bins = np.array([0, 1, 10, 100, 1000, 10000,100_000, 1_000_000, 10_000_000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "aa668815-7799-40cb-aa41-7cc19b5d2f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.cut(fec_mrbo[\"contb_receipt_amt\"], bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "94505356-a50c-4ba7-a068-8158c839fbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411         (10, 100]\n",
       "412       (100, 1000]\n",
       "413       (100, 1000]\n",
       "414         (10, 100]\n",
       "415         (10, 100]\n",
       "             ...     \n",
       "701381      (10, 100]\n",
       "701382    (100, 1000]\n",
       "701383        (1, 10]\n",
       "701384      (10, 100]\n",
       "701385    (100, 1000]\n",
       "Name: contb_receipt_amt, Length: 694282, dtype: category\n",
       "Categories (8, interval[int64, right]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "299072b7-5e95-46b8-b0e3-9d56a96cc0ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_42140\\3451390084.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped = fec_mrbo.groupby([\"cand_nm\", labels])\n"
     ]
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby([\"cand_nm\", labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "8af17978-62d4-437e-a551-0f1019999569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>(0, 1]</th>\n",
       "      <td>493</td>\n",
       "      <td>77</td>\n",
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       "      <th>(1, 10]</th>\n",
       "      <td>40070</td>\n",
       "      <td>3681</td>\n",
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       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>372280</td>\n",
       "      <td>31853</td>\n",
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       "      <th>(100, 1000]</th>\n",
       "      <td>153991</td>\n",
       "      <td>43357</td>\n",
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       "      <td>2</td>\n",
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       "      <th>(100000, 1000000]</th>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                         493            77\n",
       "(1, 10]                      40070          3681\n",
       "(10, 100]                   372280         31853\n",
       "(100, 1000]                 153991         43357\n",
       "(1000, 10000]                22284         26186\n",
       "(10000, 100000]                  2             1\n",
       "(100000, 1000000]                3             0\n",
       "(1000000, 10000000]              4             0"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.size().unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "1b23c035-8057-451c-acd9-8b6de2e76f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bucket_sums = grouped[\"contb_receipt_amt\"].sum().unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "3bea9300-4782-4e77-80de-39fde8a5fc88",
   "metadata": {},
   "outputs": [],
   "source": [
    "normed_sums = bucket_sums.div(bucket_sums.sum(axis=\"columns\"),axis=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "57783999-fd53-48c2-b8af-6c126e35115f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
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       "  <tbody>\n",
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       "      <td>0.805182</td>\n",
       "      <td>0.194818</td>\n",
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       "    <tr>\n",
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       "      <td>0.918767</td>\n",
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       "      <td>0.910769</td>\n",
       "      <td>0.089231</td>\n",
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       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>0.710176</td>\n",
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       "      <td>0.447326</td>\n",
       "      <td>0.552674</td>\n",
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       "      <th>(10000, 100000]</th>\n",
       "      <td>0.823120</td>\n",
       "      <td>0.176880</td>\n",
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      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                    0.805182      0.194818\n",
       "(1, 10]                   0.918767      0.081233\n",
       "(10, 100]                 0.910769      0.089231\n",
       "(100, 1000]               0.710176      0.289824\n",
       "(1000, 10000]             0.447326      0.552674\n",
       "(10000, 100000]           0.823120      0.176880\n",
       "(100000, 1000000]         1.000000      0.000000\n",
       "(1000000, 10000000]       1.000000      0.000000"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "normed_sums"
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   "cell_type": "code",
   "execution_count": 221,
   "id": "19d4c40b-791f-479b-a48c-44a8fb63ac91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='contb_receipt_amt'>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_sums[:-2].plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "dcf5b082-b8ab-4428-a8e8-b090babcd95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = fec_mrbo.groupby([\"cand_nm\", \"contbr_st\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "c78ddd9e-e8aa-4fa5-b28c-bdbdd4354051",
   "metadata": {},
   "outputs": [],
   "source": [
    "totals = grouped[\"contb_receipt_amt\"].sum().unstack(level=0).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "8707f719-46de-490d-bd50-a30893b93ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "totals = totals[totals.sum(axis=\"columns\") > 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "503d962f-6db6-420f-9192-142eebd1a537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>281840.15</td>\n",
       "      <td>86204.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>543123.48</td>\n",
       "      <td>527303.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>359247.28</td>\n",
       "      <td>105556.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>1506476.98</td>\n",
       "      <td>1888436.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>23824984.24</td>\n",
       "      <td>11237636.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>2132429.49</td>\n",
       "      <td>1506714.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>2068291.26</td>\n",
       "      <td>3499475.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>4373538.80</td>\n",
       "      <td>1025137.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>336669.14</td>\n",
       "      <td>82712.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>7318178.58</td>\n",
       "      <td>8338458.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK             281840.15      86204.24\n",
       "AL             543123.48     527303.51\n",
       "AR             359247.28     105556.00\n",
       "AZ            1506476.98    1888436.23\n",
       "CA           23824984.24   11237636.60\n",
       "CO            2132429.49    1506714.12\n",
       "CT            2068291.26    3499475.45\n",
       "DC            4373538.80    1025137.50\n",
       "DE             336669.14      82712.00\n",
       "FL            7318178.58    8338458.81"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "ad5a5294-039a-48d8-99d7-7ebabaf10ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "percent = totals.div(totals.sum(axis=\"columns\"), axis=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "03038280-df34-46e5-9a03-358d24066f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>0.765778</td>\n",
       "      <td>0.234222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.507390</td>\n",
       "      <td>0.492610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>0.772902</td>\n",
       "      <td>0.227098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.443745</td>\n",
       "      <td>0.556255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.679498</td>\n",
       "      <td>0.320502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0.585970</td>\n",
       "      <td>0.414030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>0.371476</td>\n",
       "      <td>0.628524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>0.810113</td>\n",
       "      <td>0.189887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.802776</td>\n",
       "      <td>0.197224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>0.467417</td>\n",
       "      <td>0.532583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK              0.765778      0.234222\n",
       "AL              0.507390      0.492610\n",
       "AR              0.772902      0.227098\n",
       "AZ              0.443745      0.556255\n",
       "CA              0.679498      0.320502\n",
       "CO              0.585970      0.414030\n",
       "CT              0.371476      0.628524\n",
       "DC              0.810113      0.189887\n",
       "DE              0.802776      0.197224\n",
       "FL              0.467417      0.532583"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a75b544-c609-4b0f-b4a5-0539e24753e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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